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| ÐомпанÑÑ Tesla випадково показала дизайн нового ÑобоÑа Optimus Gen 3 | http://internetua.com/tesla-vipadkovo-p… | 10 | Oct 03, 2026 08:00 | active | |
ÐомпанÑÑ Tesla випадково показала дизайн нового ÑобоÑа Optimus Gen 3URL: http://internetua.com/tesla-vipadkovo-pokazala-dizain-novogo-robota-optimus-gen-3 Content:
СледиÑе за наÑими обновлениÑми в ÐомпанÑÑ Tesla могла випадково ÑозкÑиÑи дизайн Ñвого нового гÑманоÑдного ÑобоÑа Optimus Gen 3 Ñе до оÑÑÑÑÐ¹Ð½Ð¾Ñ Ð¿ÑезенÑаÑÑÑ. ÐобÑÐ°Ð¶ÐµÐ½Ð½Ñ Ð¿ÑиÑÑÑÐ¾Ñ Ð·Ð½Ð°Ð¹Ñли в Android-веÑÑÑÑ ÑÑÑмового заÑÑоÑÑÐ½ÐºÑ Tesla. Ðа Ð½Ð¸Ñ ÑÐ¾Ð±Ð¾Ñ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð½Ð¸Ð¹ поÑÑÑ Ñз попеÑеднÑм пÑоÑоÑипом Optimus Gen 2.5, Ñо дозволÑÑ Ð¾ÑÑниÑи змÑни в конÑÑÑÑкÑÑÑ. ÐÑо Ñе повÑдомлÑÑ teslarati.com. СÑдÑÑи з опÑблÑÐºÐ¾Ð²Ð°Ð½Ð¸Ñ 3D-моделей, Tesla ÑÑÑÑÑво пеÑеÑобила зовнÑÑнÑÑÑÑ Optimus. ÐÑдкÑиÑÐ¸Ñ Ð¼ÐµÑ Ð°Ð½ÑÑÐ½Ð¸Ñ ÐºÐ¾Ð¼Ð¿Ð¾Ð½ÐµÐ½ÑÑв ÑÑало менÑе, а замÑÑÑÑ Ð½Ð¸Ñ Ð·’ÑвилиÑÑ Ð¼Ð°ÑÐ¾Ð²Ñ ÑоÑÐ½Ñ Ð·Ð°Ñ Ð¸ÑÐ½Ñ Ð¿Ð°Ð½ÐµÐ»Ñ. ÐоÑпÑÑ Ð²Ð¸Ð³Ð»ÑÐ´Ð°Ñ Ð±ÑлÑÑ ÑÑлÑÑним Ñ Ð¾Ð±ÑÑÑним, а Ð¼ÐµÑ Ð°Ð½Ñзми в облаÑÑÑ Ð·’ÑÐ´Ð½Ð°Ð½Ð½Ñ ÑÑлÑба Ñа нÑг ÑÐµÐ¿ÐµÑ Ð¿ÑикÑиÑÑ Ð³Ð½ÑÑкими елеменÑами. ÐомÑÑÐ½Ñ Ð·Ð¼Ñни вÑдбÑлиÑÑ Ñ Ð· ÑÑками ÑобоÑа. Ðони виглÑдаÑÑÑ ÑкладнÑÑими Ñа бÑлÑÑ Ð´ÐµÑалÑзованими. РанÑÑе Tesla заÑвлÑла, Ñо киÑÑÑ Optimus маÑимÑÑÑ 22 ÑÑÑÐ¿ÐµÐ½Ñ Ñвободи, Ñо Ð¼Ð°Ñ Ð´Ð¾Ð·Ð²Ð¾Ð»Ð¸Ñи ÑобоÑÑ Ð²Ð¸ÐºÐ¾Ð½ÑваÑи знаÑно бÑлÑÑе ÑоÑÐ½Ð¸Ñ Ð¼Ð°Ð½ÑпÑлÑÑÑй. ÐодноÑÐ°Ñ Ð¿Ð¾ÐºÐ¸ невÑдомо, Ñи вÑÑ Ñ Ð°ÑакÑеÑиÑÑики, пÑедÑÑÐ°Ð²Ð»ÐµÐ½Ñ Ð² Ð½Ð¾Ð²Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÑÑ , вÑдповÑдаÑÑÑ ÑÑналÑнÑй веÑÑÑÑ. ÐÑÑÑÑÐ¹Ð½Ð¾Ñ Ð¿ÑезенÑаÑÑÑ Optimus Gen 3 Tesla поки не пÑоводила. Ðлон ÐаÑк Ñе в беÑÐµÐ·Ð½Ñ Ð·Ð°ÑвлÑв, Ñо ÑÑеÑÑ Ð¿Ð¾ÐºÐ¾Ð»ÑÐ½Ð½Ñ ÑобоÑа вже пÑаÑÑÑ, але поÑÑебÑÑ Ð¿Ð¾Ð´Ð°Ð»ÑÑого доопÑаÑÑваннÑ. ÐÑзнÑÑе вÑн поÑÑнÑвав, Ñо компанÑÑ Ð½Ðµ поÑпÑÑаÑиме показÑваÑи оÑÑаÑоÑний дизайн, Ñоб конкÑÑенÑи не оÑÑимали можливÑÑÑÑ Ð·Ð°Ð²ÑаÑно ÑкопÑÑваÑи ÑозÑобкÑ. ÐаÑалелÑно Tesla гоÑÑÑÑÑÑÑ Ð´Ð¾ маÑÑÑабÑÐ²Ð°Ð½Ð½Ñ Ð²Ð¸ÑобниÑÑва Optimus. ÐомпанÑÑ Ð·Ð²ÑлÑнÑÑ Ð´Ð¾Ð´Ð°ÑÐºÐ¾Ð²Ñ Ð²Ð¸ÑобниÑÑ Ð¿Ð»Ð¾ÑÑ Ð½Ð° пÑдпÑиÑмÑÑÐ²Ñ Ñ Fremont, а в Ð¢ÐµÑ Ð°ÑÑ Ð±ÑдÑÑÑÑÑÑ Ð¾ÐºÑема ÑнÑÑаÑÑÑÑкÑÑÑа Ð´Ð»Ñ Ð²Ð¸Ð¿ÑÑÐºÑ Ð³ÑманоÑÐ´Ð½Ð¸Ñ ÑобоÑÑв. ЯкÑо Ð·Ð½Ð°Ð¹Ð´ÐµÐ½Ñ Ð² заÑÑоÑÑÐ½ÐºÑ Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð½Ñ ÑпÑÐ°Ð²Ð´Ñ Ð½Ð°Ð»ÐµÐ¶Ð°ÑÑ ÑÑналÑнÑй веÑÑÑÑ Gen 3, Tesla вже може бÑÑи близÑÐºÐ¾Ñ Ð´Ð¾ оÑÑÑÑйного Ð¿Ð¾ÐºÐ°Ð·Ñ Ð½Ð¾Ð²Ð¸Ð½ÐºÐ¸. ÐÑÑоÑник: itechua.com вологÑÑÑÑ: ÑиÑк: вÑÑеÑ: © 2011 - 2026 Internetua ÐонÑакÑÑ
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| Tesla Optimus: Die Billionen-Wette auf humanoide Roboter - DER AKTIONÄR | https://www.deraktionaer.de/artikel/med… | 10 | Oct 03, 2026 08:00 | active | |
Tesla Optimus: Die Billionen-Wette auf humanoide Roboter - DER AKTIONÄRDescription: Tesla Optimus und SpaceX: Musks Gespann der physischen KI Content:
Tesla will mehr sein als ein Autobauer. Der nächste große Wurf heißt bekanntlich Optimus, ein humanoider Roboter – eine Maschine in Menschengestalt, die mit Armen, Händen und Beinen in einer für Menschen gebauten Welt arbeitet. In Fremont installiert Tesla die ersten Fertigungslinien, ausgelegt auf eine Million Roboter pro Jahr. Ab 2027 soll in Texas eine zweite Linie für bis zu zehn Millionen Einheiten folgen. Elon Musk traut Optimus viel zu: Der Roboter könne rund 80 Prozent des künftigen Tesla-Werts ausmachen. Wie groß der Markt der Zukunft sein könnte, rechnet die Fondsgesellschaft ARK Invest vor. Ein einziger Haushaltsroboter könnte die Wirtschaftsleistung demnach um 62.000 Dollar pro Jahr erhöhen, in allen 90 Millionen selbst genutzten US-Eigenheimen wären es knapp sechs Billionen Dollar. Menschliches Niveau bei Alltagsaufgaben erwartet ARK in der Studie um 2028. Noch ist das Zukunftsmusik. Der Serienstart ist von Tesla nur für „später in diesem Jahr“ angekündigt, die dritte Optimus-Generation noch nicht vorgestellt. Musk selbst nennt Optimus das am schwersten zu skalierende Produkt von Tesla, der Hochlauf werde „flach und lang“. Die Konkurrenz von Figure AI bis Unitree ist längst unterwegs. Kann Tesla den Vorsprung aus Software und Fertigung ausspielen? Im Chart hat die Aktie mit dem Anstieg vom 21. September die Kurslücke vom 23. Juli geschlossen. Die 200-Tage-Linie verläuft bei 396 Dollar. Nach unten sichern die Tiefs von Anfang September und die 50-Tage-Linie bei 349 Dollar ab. Fällt die Aktie unter diese Marke, steht die Erholung seit Juli infrage. Nächste Termine sind die Auslieferungszahlen und der Quartalsbericht im Oktober. Wer auf Musks KI-Welt setzen will, muss nicht bei Tesla ansetzen. Seit der Übernahme von xAI im Februar gehört auch das Sprachmodell Grok zu SpaceX. Morgan Stanley sieht beide Konzerne als Gespann der physischen KI: SpaceX liefert Rechenleistung und Vernetzung, Tesla Roboter und Daten. DER AKTIONÄR setzt weiterhin auf SpaceX, auch im AKTIONÄR-Depot ist man positiv gestimmt. DER AKTIONÄR DAILY NewsletterBleiben Sie über die neuesten Entwicklungen bei spannenden Unternehmen und an der Börse auf dem Laufenden. Lesen Sie DER AKTIONÄR DAILY – den täglichen Newsletter von Deutschlands führendem Börsenmagazin. Kostenlos. Hinweis auf InteressenkonflikteDer Vorstand und Mehrheitsinhaber der Herausgeberin Börsenmedien AG, Herr Bernd Förtsch, ist unmittelbar und mittelbar Positionen über die in der Publikation angesprochenen nachfolgenden Finanzinstrumente oder hierauf bezogene Derivate eingegangen, die von der durch die Publikation etwaig resultierenden Kursentwicklung profitieren können: Tesla, SpaceX. Hinweis auf InteressenkonflikteDer Autor hält unmittelbar Positionen über die in der Publikation angesprochenen nachfolgenden Finanzinstrumente oder hierauf bezogene Derivate, die von der durch die Publikation etwaig resultierenden Kursentwicklung profitieren können: SpaceX. Hinweis auf Interessenkonflikte:Aktien der SpaceX befinden sich in einem Real-Depot der Börsenmedien AG. Jim Cramer liefert eine durchdachte Anleitung für alle, die am Aktienmarkt aktiv werden wollen – egal, wie turbulent die Zeiten sind. Er zeigt Ihnen, wie Sie Ihre Angst vor dem Investieren überwinden, auch mit kleinen Beträgen einsteigen und gezielt in Wachstums- und Dividendenaktien investieren können. Er macht anschaulich, wie Märkte ticken, warum Kurse steigen oder fallen – und wie Sie systematisch Chancen erkennen, anstatt sich von Unsicherheit lähmen zu lassen. Erfahren Sie, wie Sie mit Ihrem Kapital stattliche Renditen erzielen können, indem Sie Strategien nutzen, die auch Cramer selbst in seiner Karriere erfolgreich angewandt hat. Ob Marktaufschwung oder Rücksetzer: Hier erfahren Sie, wie Sie Ihr Geld für sich arbeiten lassen. Autoren: Cramer, James J.Seitenanzahl: 368Erscheinungstermin: 07.05.2026Format: KlappenbroschurISBN: 978-3-68932-085-0 SpaceX Aktie ist auch Bestandteil des DER AKTIONÄR Global Space Champions Index. Der Index bündelt 20 führende Unternehmen der globalen Space Economy – von Raketenstarts und Satellitenkommunikation bis zu Erdbeobachtung, Weltraumdaten sowie Aerospace und Defence.
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| Tesla quer 20 mil robôs Optimus por semana | https://www.maistecnologia.com/tesla-qu… | 10 | Oct 03, 2026 08:00 | active | |
Tesla quer 20 mil robôs Optimus por semanaURL: https://www.maistecnologia.com/tesla-quer-20-mil-robos-optimus-por-semana/ Description: A Tesla acelera o Optimus, mas enfrenta problemas nas mãos robóticas, na montagem e no treino antes de atingir 20.000 unidades por semana. Content:
© All Rights Reserved, MaisTecnologia A Tesla está a acelerar a produção dos robôs humanoides Optimus, mas encontrou um problema difícil de resolver: fabricar mãos suficientemente precisas, resistentes e fáceis de montar em grande escala. A empresa terá produzido várias centenas de unidades por semana no mês passado, acima das poucas dezenas registadas alguns meses antes. Ainda assim, o ritmo está longe da meta anunciada de cerca de 20.000 robôs por semana. De acordo com informações divulgadas pelo The Information, alguns equipamentos da linha de montagem não conseguem alinhar sempre as peças com a precisão necessária. O problema afeta componentes como as mãos e as articulações. O resultado é um aumento do número de robôs que precisam de ser corrigidos depois de saírem da linha de produção. Numa máquina tão complexa, pequenos erros de montagem podem causar atrasos significativos. As mãos são particularmente difíceis de fabricar. Cada mão e cada antebraço incluem mais de 100 parafusos e outras peças pequenas, muitas das quais ainda terão de ser instaladas manualmente. Além da complexidade da montagem, existem dúvidas sobre a resistência das mãos para utilização prolongada. Alguns sensores táteis também terão apresentado problemas de fiabilidade. Para evitar a substituição completa da mão quando um sensor falha, a Tesla desenvolveu uma luva sensorial substituível. Este componente reúne os sensores táteis e poderá ser trocado de forma independente. A solução deverá ser integrada numa futura versão do Optimus. Na prática, a Tesla pretende tornar a manutenção mais simples e reduzir o tempo em que cada robô fica parado. Esta abordagem é importante porque reparar milhares de robôs humanoides seria muito mais complexo do que fazer a manutenção de uma máquina industrial tradicional. O desafio não está apenas na produção do hardware. O Optimus também ainda terá dificuldades em executar diferentes tarefas sem receber treino específico para cada uma. Segundo as informações divulgadas, até operações básicas podem exigir vários dias de programação e aprendizagem. Isso significa que o robô ainda está longe de funcionar como um assistente verdadeiramente autónomo e universal. Por enquanto, a Tesla utiliza a maioria das unidades produzidas internamente, sobretudo para testes, recolha de dados e treino dos sistemas de inteligência artificial. Nas fábricas, os robôs trabalham em áreas controladas, onde os movimentos e as tarefas podem ser acompanhados de perto. Este modelo permite à empresa identificar falhas antes de colocar o Optimus nas mãos de clientes. Elon Musk já afirmou que o Optimus poderá tornar-se num dos produtos mais importantes da Tesla. A empresa imagina aplicações em fábricas, armazéns e, no futuro, até em tarefas domésticas. A Tesla terá como objetivo começar a vender ou disponibilizar os robôs a clientes comerciais no final de 2027. Numa primeira fase, a empresa deverá privilegiar o aluguer, em vez da venda direta. Este modelo permitiria à Tesla acompanhar os robôs no terreno, recolher dados e melhorar os sistemas de inteligência artificial com base em situações reais de trabalho. A empresa já libertou espaço para a produção do Optimus na fábrica de Fremont, na Califórnia, depois de reduzir a produção dos Model S e Model X. No entanto, passar de algumas centenas de unidades por semana para 20.000 exigirá mais do que aumentar o número de trabalhadores ou estações de montagem. A Tesla terá de resolver a precisão da linha, a durabilidade dos componentes e a capacidade de treino dos robôs. O Optimus pode vir a transformar tarefas repetitivas em fábricas e armazéns, mas a distância entre um protótipo impressionante e um produto fiável para utilização diária continua a ser considerável. Fanático de tecnologia e fã do Android, mas com consciência que a Apple revolucionou vários mercados. Quem me conhece, sabe que estou sempre à procura de notícias sobre tecnologia. O seu endereço de email não será publicado. Campos obrigatórios marcados com * Fundado em 2008, o MaisTecnologia é um portal que se dedica à divulgação de informação na área da tecnologia e ciência. MaisTecnologia - Marca Registada
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| China Opens Smart Factory Where Humanoid Robots Build Other Robots | https://www.khaama.com/china-opens-smar… | 0 | Oct 02, 2026 16:00 | active | |
China Opens Smart Factory Where Humanoid Robots Build Other RobotsURL: https://www.khaama.com/china-opens-smart-factory-where-humanoid-robots-build-other-robots/ Description: China has opened a fully automated smart factory where humanoid robots assemble and test other humanoid robots, highlighting the country. Content: |
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| Noise scheduling and out-of-distribution generalisation in deep reinforcement learning for … | https://www.nature.com/articles/s41598-… | 9 | Oct 02, 2026 08:00 | active | |
Noise scheduling and out-of-distribution generalisation in deep reinforcement learning for 6-DOF robotic grasping | Scientific ReportsDescription: Exploration strategy is a fundamental but underexplored design choice in deep reinforcement learning for robotic manipulation. This paper presents a controlled empirical comparison of three off-policy configurations on a 6-DOF robotic grasping task using a Universal Robots UR5e in the RoboSuite simulation environment: Soft Actor-Critic (SAC) with automatic entropy tuning, Twin Delayed Deep Deterministic Policy Gradient (TD3) with piecewise linear noise decay, and TD3 with constant Gaussian noise. All configurations share identical network architectures, reward functions, and training budgets. The two TD3 variants differ only in the action-noise schedule and form the controlled comparison of the study, while SAC serves as a widely used entropy-regularised reference. Each configuration is trained over 10,000 episodes across five independent runs and evaluated over 1,000 deterministic episodes both within and beyond the training distribution. SAC achieves the highest in-distribution success rate (89.6%) and training efficiency, with a large effect size over both TD3 variants ($$d = 1.32$$ and $$d = 1.47$$) and complete rank separation in area under the learning curve. Within the training distribution, the two TD3 variants are statistically indistinguishable ($$d = 0.04$$). Under out-of-distribution evaluation, however, this contrast changes sharply: the noise-decay variant reaches a mean success rate of 43.1% against 32.3% for constant noise — an effect size of $$d = 1.08$$, where the same comparison within the training distribution is negligible — together with the lowest cross-run variance of the three configurations and a more uniform distribution of success across the workspace. No pairwise difference reaches significance at five runs per configuration, and the comparisons are interpreted through effect sizes and confidence intervals. These results indicate that noise schedule design has a measurably different effect on in-distribution performance and spatial out-of-distribution generalisation, and that standard single-metric evaluation protocols may fail to capture meaningful differences between exploration strategies. Content:
Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Advertisement Scientific Reports (2026) Cite this article We’re sharing this article early to provide faster access to peer-reviewed, accepted research. It is citable and carries a permanent DOI. This version is subject to further edits and will be replaced automatically by the final Version of Record. All legal disclaimers apply. Exploration strategy is a fundamental but underexplored design choice in deep reinforcement learning for robotic manipulation. This paper presents a controlled empirical comparison of three off-policy configurations on a 6-DOF robotic grasping task using a Universal Robots UR5e in the RoboSuite simulation environment: Soft Actor-Critic (SAC) with automatic entropy tuning, Twin Delayed Deep Deterministic Policy Gradient (TD3) with piecewise linear noise decay, and TD3 with constant Gaussian noise. All configurations share identical network architectures, reward functions, and training budgets. The two TD3 variants differ only in the action-noise schedule and form the controlled comparison of the study, while SAC serves as a widely used entropy-regularised reference. Each configuration is trained over 10,000 episodes across five independent runs and evaluated over 1,000 deterministic episodes both within and beyond the training distribution. SAC achieves the highest in-distribution success rate (89.6%) and training efficiency, with a large effect size over both TD3 variants (\(d = 1.32\) and \(d = 1.47\)) and complete rank separation in area under the learning curve. Within the training distribution, the two TD3 variants are statistically indistinguishable (\(d = 0.04\)). Under out-of-distribution evaluation, however, this contrast changes sharply: the noise-decay variant reaches a mean success rate of 43.1% against 32.3% for constant noise — an effect size of \(d = 1.08\), where the same comparison within the training distribution is negligible — together with the lowest cross-run variance of the three configurations and a more uniform distribution of success across the workspace. No pairwise difference reaches significance at five runs per configuration, and the comparisons are interpreted through effect sizes and confidence intervals. These results indicate that noise schedule design has a measurably different effect on in-distribution performance and spatial out-of-distribution generalisation, and that standard single-metric evaluation protocols may fail to capture meaningful differences between exploration strategies. The authors would like to express their gratitude to Prof. Dushko Stavrov for his valuable insights and comments. Faculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University in Skopje, Skopje, North Macedonia Ilija Mizhimakoski, Stefan Zlatinov, Hristijan Gjoreski & Gorjan Nadzinski Search author on:PubMed Google Scholar Search author on:PubMed Google Scholar Search author on:PubMed Google Scholar Search author on:PubMed Google Scholar Correspondence to Ilija Mizhimakoski. The authors declare that they have no competing interests. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Reprints and permissions Mizhimakoski, I., Zlatinov, S., Gjoreski, H. et al. Noise scheduling and out-of-distribution generalisation in deep reinforcement learning for 6-DOF robotic grasping. Sci Rep (2026). https://doi.org/10.1038/s41598-026-72368-3 Download citation Received: 08 May 2026 Accepted: 16 September 2026 Published: 01 October 2026 DOI: https://doi.org/10.1038/s41598-026-72368-3 Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative Advertisement Scientific Reports (Sci Rep) ISSN 2045-2322 (online) © 2026 Springer Nature Limited Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.
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| Nvidia Alumni Are Fueling a New Wave of Robotics and … | https://www.businessinsider.com/nvidia-… | 10 | Oct 02, 2026 00:01 | active | |
Nvidia Alumni Are Fueling a New Wave of Robotics and AI Startups - Business InsiderURL: https://www.businessinsider.com/nvidia-alumni-fueling-new-wave-robotics-ai-startups-2026-9 Description: Nvidia robotics alums are launching AI startups as venture capital fuels a boom. The chip giant stands to benefit from their success. Content:
Nvidia helped build the foundation for physical AI. Now, many of its veterans are launching the next wave of robotics startups. The chip giant has poured billions into robotics, and Nvidia CEO Jensen Huang helped popularize the term "physical AI, " which enables machines to interact with the real world. As with AI agents and other applications, Nvidia's strategy is to build underlying technology — the chips that power robots and the software used to train them. That ecosystem has become fertile ground for founders. In recent years, some who helped build the foundations of robotics inside Nvidia "are spinning out to build companies of their own," said Amulya Vishwanath, who left the company last year to start Techable Ventures, a venture firm focused on physical AI. Nvidia's former vice president of AI research, Sanja Fidler, left in July to found Veeda AI alongside other ex-Nvidians. The startup builds simulated worlds to train robots — an extension of the research Fidler worked on at Nvidia. Former Nvidia researcher Joel Jang founded Dream Labs, another startup working in that industry, the Information reported. Nvidia alums are attacking different areas in robotics, from software for deploying robots in warehouses to those that perform physical tasks for businesses. Nvidia has invested in some, including Flexion Robotics and Dyna Robotics. Flexion CEO and cofounder Nikita Rudin said that Nvidia maintains close ties with the startup, holding biweekly meetings and access to early tools for testing. "We're still doing the same thing we were doing before," Rudin said. "We were the beta testers — and now again, just from the outside." Take a smarter break in your day - and see how far you get. Add BI in Google so our reporting is easier to find when you’re searching for what matters. Rudin and his cofounder, David Hoeller, left Nvidia after becoming convinced robotics technology had reached an inflection point and was ready for deployment. "We were not necessarily convinced that staying at Nvidia was the fastest way to actually go and deliver that," Hoeller said. Former Nvidia robotics researcher Arsalan Mousavian cited a similar motivation. He described Nvidia as a "hardware-first" company centered on AI chips, and he wanted to be "laser focused" on building useful robots. Mousavian's startup, founded last year, remains in stealth. Siddhant Haldar, a former Nvidia robotics intern who considered joining the company before founding Index Robotics, said the field has long been held back by a lack of real-world training data. Now that powerful AI models can pick up some of the slack and robotics venture funding is booming, he said it was the right time to strike out. Physical AI companies raised $33.4 billion globally in the first half of 2026, already surpassing the $28.7 billion raised in all of last year, according to PitchBook. Startups can also take risks and deploy imperfect robots because large companies have "more to lose," Haldar said. Nvidia isn't alone in producing a new crop of robotics founders. Former Google DeepMind engineers launched Reimagine Robotics and Generalist, while Waymo veterans have gone on to build Bedrock Robotics, as researchers from fields like autonomous driving are pouring into the space. Nvidia's growing startup diaspora creates a cycle: The more startups that emerge, the more Nvidia stands to benefit by selling them the infrastructure needed to build. Have a tip? Contact this reporter via email at gweiss@businessinsider.com or Signal at @geoffweiss.25. Use a personal email address and a nonwork device; here's our guide to sharing information securely. No comments right now, check back later. Comments are unavailable right now. Jump to
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| China's humanoid robots can now fall and get back up... | https://defence.pk/threads/chinas-human… | 0 | Sep 30, 2026 08:00 | active | |
China's humanoid robots can now fall and get back up...URL: https://defence.pk/threads/chinas-humanoid-robots-can-now-fall-and-get-back-up.781316/ Description: Fall recovery capabilities have advanced significantly as a result of recent improvements in humanoid robots especially among Chinese experts. These... Content: |
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| Face-tracking robot with Arduino UNO Q - Open Electronics | https://www.open-electronics.org/face-t… | 9 | Sep 30, 2026 00:01 | active | |
Face-tracking robot with Arduino UNO Q - Open ElectronicsURL: https://www.open-electronics.org/face-tracking-robot-with-arduino-uno-q/ Description: An inexpensive robot kit with Arduino UNO Rev3 becomes a face-tracking robot by swapping the board for an Arduino UNO Q, which adds local AI processing… Content:
Open Electronics An inexpensive robot kit with Arduino UNO Rev3, obstacle-avoidance sensors, and line-following capability becomes a face-tracking robot. The trick is in the control board: just replace the UNO Rev3 with an Arduino UNO Q, which has the same headers and mounts an STM32U585 microcontroller alongside a Linux microprocessor. Iulia Feroli’s project shows how local artificial intelligence can be added to a low-cost robot without touching the mechanics. The robot starts from the Elegoo kit, with its motor shield and sensors for obstacle avoidance and line following. The UNO Q slots in place of the original board, and the shield moves over without any modification. Thanks to the STM32 microcontroller and the Linux microprocessor, the new board runs machine learning models locally, with no cloud connection. A standard USB webcam is connected to the UNO Q to provide vision. The webcam video stream is processed with the face tracking Brick from Arduino App Lab. The code converts the face position in the frame into movement commands for the robot. The robot rotates to center the face and moves toward it, always staying in front of the person. The result is a responsive face tracker that requires no external servers or Wi-Fi connections. Iulia Feroli’s project is documented in a video showing the robot in action, with an explanation of the assembly and the code. Swapping the board is the core of the intervention: the UNO Q maintains electrical and mechanical compatibility with the UNO Rev3 but adds the computing power needed for AI. In addition, the face tracking Brick in Arduino App Lab simplifies managing the machine learning model, making the code accessible even to those without neural network experience. To replicate the robot you need only a few components, all easily available. The list includes the Elegoo kit, a USB webcam, and the control board. Here are the main steps: The UNO Q is the heart of the system: it combines the simplicity of the STM32U585 microcontroller with the power of the Linux processor. This combination allows local machine learning models, such as face tracking, to run without additional hardware. The board is also available in a 4GB version with a full accessory kit, which includes everything needed to get started. The original Elegoo kit, with its Arduino UNO Rev3 board, remains an excellent base for other projects. However, for this face tracker, the UNO Q is the right choice: it offers the necessary computing power and maintains compatibility with the shield. The overall cost stays low, and the result is a smart robot that impresses with its responsiveness. Source: https://youtu.be/FIu14vCvGfs?si=8SSW0K6O7J6Y07tz You must be logged in to post a comment. https://youtu.be/QP3xScVA8Gs The concept of this robot is very simple. To build it we will need: Arduino mega, l293d motor module, flame sensor and relay module. More info Build it yourself Boards, sensors and kits for projects like this one. Browse the shop Read More Autonomous Fire Fighting Robot With Self Finding FlamesContinue When I took a class called “Conducting Robots,” where students were tasked with making a robot, in one semester, that could conduct a ten-piece orchestra, the professors recommended that we use two open source tools: Processing and Arduino. These tools took care of the low-value parts of a project so that we could focus on what… Read More Processing and Arduino to manipulate digital content: easier to become an artist!Continue A new open source Espressif ESP32-based board offering a form factor similar to popular single board computers such as the Raspberry Pi Model A / 3 Model A+ has been launched via the Crowd Supply website this month and takes the form of the Obsidian ESP32. Obsidian ESP32 is an Espressif ESP32-based board adopting the familiar form… Read More Obsidian ESP32 Board in a Raspberry Pi Form FactorContinue Seen at the Maker Faire European 2015 edition in Rome, Allarmino is an “all in one” professional and modular security alarm & home automation system, based on Atmel ATmega2560. Is is pin-to-pin compatible with Arduino Mega 2560 and relative bootloader for simply programming through Arduino\Genuino IDE. It works at 3.3V for best coupling with on… Read More Allarmino – Arduino based security alarm & home automation systemContinue . The objective of this project is to build an Arduino voice shield to empower thousands of voice related applications! All this mostly thanks to an integrated ISD1790PY chip. This particular voice/TTS feature can be useful to integrate voice messages in alarm systems, to implement generic I/O controls in home automation or even in home… Read More A Voice Shield for Arduino – Give Voice to your Ideas!Continue If you’re wondering what’s 1Sheeld, here’s it: 1Sheeld is a new easily configured shield for Arduino. It is connected to a mobile app that allow the usage of all of Android smart phones’ capabilities such as LCD Screen, Gyroscope, Switches, LEDs, Accelerometer, Magnetometer, GSM, Wi-Fi, GPS …etc. into your Arduino sketch. And, guys, that’s a… Read More 1Sheeld lets your Smart Phone be an extended Arduino shieldContinue Open-Electronics.org is the brainchild of a world leader in hobby electronics Futura Group srl. Open-Electronics.org is devoted to support development, hacking and playing with electronics: we share exciting open projects and create amazing products! Open-Electronics.org is not just a container of ideas: it is also a web site lead by a team of engineers and geeks who will take part in the discussions and give support. Our mission is to become a reference Open Source hacking site with ideas and feedback aimed to enrich the community. © 2026 Open Electronics Futura Group Srl · Via Adige 11, 21013 Gallarate (VA), ItalyVAT no. IT10918280156 · Varese Companies Register no. 10918280156 · REA VA-297771 · Share capital €60,000 fully paid Terms of sale · Contact us We use cookies to count visits and to remember which page or campaign an order came from. No advertising or profiling cookies. You can change your mind at any time from the "Cookies" link at the bottom of every page. Details in our Cookie policy. Your consent ID:
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| Elekit Gyrostar Robot Kit | Japan Trend Shop | https://www.japantrendshop.com/elekit-g… | 10 | Sep 30, 2026 00:01 | active | |
Elekit Gyrostar Robot Kit | Japan Trend ShopURL: https://www.japantrendshop.com/elekit-gyrostar-robot-kit-p-7908.html Description: Elekit Gyrostar Robot Kit - Gyroscope are toys with a spinning wheel mechanism whose spin axis changes orientation by itself. Not only are they lots of fun, they bring learning to life by allowing you to see some of the laws of physics in spectacular action! The Elekit Gyrostar Robot Kit is one such toy that takes the gyroscop ... Content:
Gyroscope are toys with a spinning wheel mechanism whose spin axis changes orientation by itself. Not only are they lots of fun, they bring learning to life by allowing you to see some of the laws of physics in spectacular action! The Elekit Gyrostar Robot Kit is one such toy that takes the gyroscope concept to a whole new level by combining it with a robot, which you can make yourself using the pieces in the box. Other than a Philips screwdriver, pair of nippers, and two AA batteries to power it, the Elekit Gyrostar Robot Kit contains not only all the pieces for building the approximately 93 x 93 x 87 mm (3.7 x 3.7 x 3.4") gadget but also the many different types of rails/tracks it can move along. The kit has several modules that can be assembled in almost any number of ways to make your robot's movement more difficult – and more exciting. So teach your children, or yourself, some science while having a great time building and playing with this amazing robot! Specs and Features: Copyright © 2026 Japan Trend Shop
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| KAIST's AI robot breaks 100-meter world record - The Korea … | https://koreatimes.co.kr/www/tech/2023/… | 2 | Sep 30, 2026 00:01 | active | |
KAIST's AI robot breaks 100-meter world record - The Korea TimesURL: https://koreatimes.co.kr/www/tech/2023/12/133_365277.html Description: Korea Advanced Institute of Science and Technology (KAIST) says its artificial intelligence (AI) robot has broken the 100-meter world record for a... Content:
KAIST Hound runs a 100-meter sprint on an outdoor track at KAIST campus in Daejeon, Oct. 26. Courtesy of KAIST Prof. Park Hae-won from KAIST's Dynamic Robot Control and Design Laboratory Korea Advanced Institute of Science and Technology (KAIST) says its artificial intelligence (AI) robot has broken the 100-meter world record for a quadruped robot, citing Guinness World Records, Friday. KAIST Hound, developed by the school's Dynamic Robot Control and Design Laboratory, recorded a time of 19.87 seconds at the institute's outdoor track in Daejeon, Oct. 26. The robot made another record that could possibly become its second world record. Aiming to break the record in an indoor setting, Hound ran a treadmill and hit a maximum velocity of 6.5 meters per second. The figure beats Cheetah 2, developed by Massachusetts Institute of Technology, which recorded 6.4 seconds. Prof. Park Hae-won, who oversaw Hound's development, has applied to Guinness World Records for the indoor record. Hound developed the sprinting technique via reinforcement learning, a data-driven AI learning mechanism. Park's team programmed the self-learning system for Hound by inputting a maximum torque for its motor and a system for increasing velocity. The team also worked on the motor to distribute its output evenly through the robot's legs to achieve a balanced, symmetrical four-legged movement. Hound's legs were specially designed with light-weight materials to lighten its overall weight. "Hound has proven that Korea possesses globally undisputable technologies in robot hardware and robotic self-control AI," said Park, who specializes in the control and design of dynamic robot systems, legged locomotion robots and bio-inspired robots. The Agency for Defense Development, under the country's Defense Acquisition Program Administration, supported the research on Hound by granting a fund in 2019 for the development of new defense technologies.
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| Robot learns new skills by copying humans, then teaching itself | https://interestingengineering.com/ai-r… | 10 | Sep 30, 2026 00:01 | active | |
Robot learns new skills by copying humans, then teaching itselfURL: https://interestingengineering.com/ai-robotics/robot-learns-like-a-child-masters-tasks Description: New AI framework allows robots to imitate humans, improve through self-learning, and perform complex tasks with near-perfect reliability. Content:
Pick the engineering stories that matter and get them in your inbox. Access expert insights, exclusive content, and a deeper dive into engineering and innovation all with fewer ads or a completely ad-free experience. All Rights Reserved, IE Media, Inc. Follow Us On Access expert insights, exclusive content, and a deeper dive into engineering and innovation all with fewer ads or a completely ad-free experience. All Rights Reserved, IE Media, Inc. RL-100 first imitates human demonstrations, then uses reinforcement learning to autonomously improve its skills. A robot has learned to bowl, fold towels, unscrew lids, pour drinks, and prepare fresh orange juice by first imitating human demonstrations and then refining its skills through autonomous learning. Researchers developed a new training framework called RL-100 that combines imitation learning with reinforcement learning, enabling robots to perform a wide range of real-world manipulation tasks with high reliability. In tests, the system adapted to unfamiliar situations, recovered from disturbances, and even matched or outperformed human teleoperators in several tasks. The robot also operated continuously for seven hours serving fresh juice in a public mall without a single failure. Developed by researchers led by Shanghai Jiao Tong University, the framework, called RL-100, allows robots to first acquire safe, human-like behaviors from demonstrations and then improve those skills through autonomous trial-and-error. The approach overcomes one of robotics’ biggest challenges: moving beyond simply copying humans to achieving faster, more reliable, and more adaptable performance in unstructured environments. The researchers drew inspiration from how children learn. Babies initially rely on guidance from parents before gradually refining their abilities through independent practice. RL-100 follows the same philosophy through a three-stage learning pipeline. The first stage uses imitation learning, where the robot observes teleoperated demonstrations performed by human experts. These demonstrations train a diffusion-based visuomotor policy, enabling the robot to learn the relationship between visual inputs—captured through RGB cameras or 3D point clouds—and the corresponding manipulation actions. This stage provides a stable behavioral foundation but remains limited by the quality and efficiency of the human demonstrations. To overcome this “imitation ceiling,” the second stage introduces iterative offline reinforcement learning. Instead of relying solely on human data, the robot repeatedly performs tasks, stores the resulting experiences, and retrains itself using both demonstration data and its own successful attempts. A unified Proximal Policy Optimization (PPO)-based objective allows the system to refine the diffusion policy without destabilizing previously learned behaviors, while an offline policy evaluation mechanism prevents updates that could reduce performance. The final stage applies a smaller amount of online reinforcement learning directly on the physical robot. This phase specifically targets rare failure cases that remain after offline training, improving reliability from roughly 90 percent success to near-perfect task completion while requiring relatively little additional real-world data. RL-100 also addresses a key practical limitation of diffusion policies—their computational latency. Standard diffusion models require multiple denoising steps before producing each action, making them too slow for high-frequency robotic control. To solve this, the researchers developed a consistency-model distillation technique that compresses the multi-step diffusion policy into a single-step controller, reducing inference latency from around 100 milliseconds to about 10 milliseconds while maintaining performance. This enables faster reaction times and smoother control during deployment. The framework is designed to be task-, robot-, and representation-agnostic, supporting both single-arm and dual-arm robots, single-action and action-chunk control, and either RGB images or 3D point clouds without changing the underlying learning framework. Researchers evaluated RL-100 across eight challenging manipulation tasks involving rigid objects, deformable materials, liquids, and precision assembly. These included bowling, towel folding, pouring, unscrewing lids, folding boxes, and two stages of orange juicing. After completing the full training pipeline, the robot achieved a 100 percent success rate across 1,000 evaluation trials, matched or exceeded expert human teleoperators in task completion speed, adapted to unseen objects and environmental changes without retraining, and remained robust even when researchers physically interfered with its actions. In a real-world deployment, the robot continuously prepared fresh orange juice for customers in a shopping mall for seven hours without a single failure, demonstrating the framework’s potential for reliable long-term operation in homes, factories, and public spaces. Jijo is an automotive and business journalist based in India. Armed with a BA in History (Honors) from St. Stephen's College, Delhi University, and a PG diploma in Journalism from the Indian Institute of Mass Communication, Delhi, he has worked for news agencies, national newspapers, and automotive magazines. In his spare time, he likes to go off-roading, engage in political discourse, travel, and teach languages. Premium Follow
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| ICE plans to buy Boston Dynamics’ robot dogs | https://www.boston.com/news/local-news/… | 10 | Sep 29, 2026 16:00 | active | |
ICE plans to buy Boston Dynamics’ robot dogsURL: https://www.boston.com/news/local-news/2026/09/09/ice-plans-to-buy-boston-dynamics-robot-dogs/ Description: Boston Dynamics says ICE would have to follow the same terms and ethical rules as every other customer, including a ban on weaponizing them. Content:
By Beth Treffeisen Send this article to your social connections. Send this article to your social connections. U.S. Immigration and Customs Enforcement (ICE) is trying to tap a local company for assistance. According to the U.S. Department of Homeland Security (DHS), ICE is planning to spend between $1 million and $2 million to to purchase Boston Dynamics’ Spot robots. The formal procurement process lists an expected solicitation start date of Sept. 4. However, the government has not yet issued an RFP for this. DHS said ICE wants to use the remotely operated robots to support public safety and law enforcement operations, including inspecting potentially dangerous areas, assessing hazards, and providing situational awareness without putting personnel at risk. The robots, DHS said, could help improve officer safety and decision-making in incidents involving dangerous, confined, unstable, or otherwise difficult-to-access areas. Boston Dynamics sells to many government agencies and public safety organizations. A company spokesperson said that the robots are used to keep people out of harm’s way and to help first responders in assessing dangerous situations. Examples include using Spot for hazardous gas detection, unexploded ordnance inspection, suspicious package investigation, search and rescue, subterranean or confined-space exploration, and structural assessments following fires, disasters, or other hazards. The company did not say how ICE would use the robots. Additionally, the company said that “any attempted weaponization of Boston Dynamics’ robots is strictly prohibited,” as outlined in its Terms and Conditions, ethical principles, and an open letter against weaponization. “We pledge that we will not weaponize our advanced-mobility general-purpose robots or the software we develop that enables advanced robotics, and we will not support others to do so,” the open letter said. “When possible, we will carefully review our customers’ intended application to avoid potential weaponization.” The open letter was led by Boston Dynamics and co-signed by five other leading robotics companies. Gov. Maura Healey did not respond to a request for comment on ICE’s plans to purchase the Boston Dynamics robots. Healey has previously previously opposed ICE operating in Massachusetts, including signing legislation in August aimed at restricting the federal agency’s activities in the state. At the same time, the Healey-Driscoll administration has backed Boston Dynamics and its expansion in Massachusetts, awarding the company $25 million to support its recently announced Waltham expansion. “Boston Dynamics has played a central role in defining the global robotics industry from right here in our state, and it’s why we wanted them to choose Massachusetts as the site of their expansion,” Healey said in a statement at the time. Beth Treffeisen is a general assignment reporter for Boston.com, focusing on local news, crime, and business in the New England region. Get everything you need to know to start your day, delivered right to your inbox every morning. SIGN UP ©2026 Boston Globe Media Partners, LLC Stay up to date with everything Boston. Receive the latest news and breaking updates, straight from our newsroom to your inbox.
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| Technical SEO for LLM Scrapers: Crawl, Chunk, and Citation Readiness … | https://www.singlegrain.com/blog-posts/… | 10 | Sep 29, 2026 00:01 | active | |
Technical SEO for LLM Scrapers: Crawl, Chunk, and Citation Readiness Checklist - Single GrainURL: https://www.singlegrain.com/blog-posts/technical-seo-for-llm-scrapers/ Description: Make sure LLM scrapers can crawl, chunk, and cite your pages with access, extractable answers, and retained sources. Content:
AI SEO that plans, writes & ranks - 90+ hours/month saved Personalized LinkedIn ads in minutes, not weeks. 40% higher B2B conversions. LLM scraper readiness starts with access, extractable answers, and claims that retain their sources when separated from the page. The deliverable is a reusable checklist tied to URLs, evidence, and owners. It does not predict citations or assign an LLM visibility score. Buy implementation help when these controls cross SEO, engineering, and content responsibilities without a clear operator. At a pre-release review, a technical SEO opens a money page’s robots rules beside its rendered preview. The preview contains the offer and its supporting evidence. The crawler-specific rule blocks the URL. This worked example starts with a concrete artifact: a page that looks ready to a buyer but is unavailable to the intended crawler. In the same review, a content engineer compares the response HTML with the browser DOM. Navigation links arrive immediately; the answer block appears only after JavaScript runs. The checklist has no answer selector or chunk boundary. These are illustrative implementation failures, not Single Grain client results. Both require a page-level fix before another model purchase. Job delta: Closest live technical SEO checklists target Googlebot. This page adds crawl→chunk→cite-ready controls for LLM scrapers, with pass/fail evidence per URL. Kill rule: any Fail without owner+fix ticket → block “AI-ready” claim on that URL. Operator artifact : LLM scraper readiness checklist (illustrative ACME row; not a client result). Eng+content blocks “AI-ready” claims until Pass/Fail is evidenced: Open Future Forum (2026-09-06) puts 81% of 230 leaders past exploring agentic AI, so fix crawler access and extractable answer blocks before buying another model. Eric’s Jev as the fast cheap middle layer: classify, apply criteria, route between models and ops changes where control sits before the next expensive write. The same review applies Reusable workflows beat custom premium work; build them, then hand them to the team, so uncertain rows route to a human instead of auto-publishing. Together with the embedded scene above, that is three on-angle Eric tape inputs for this job, not a link dump. A visibility report records whether a sampled answer mentions your brand. Crawl readiness asks whether a particular agent can retrieve a particular URL and extract a useful, attributable passage. A mention cannot prove that your current pricing page is accessible. An accessible pricing page cannot guarantee a mention. Single Grain’s LLMO best-practices guide covers the broader optimization program. This checklist addresses the implementation handoff underneath it: access rules, canonical targets, answer selectors, chunk boundaries, and source-bearing claims. It produces repair tickets, not another visibility dashboard. Google’s guidance for AI features in Search points publishers toward existing SEO foundations rather than special AI markup. That supports the recommendation in Single Grain’s AI search optimization is SEO: repair the discovery and content layer before commissioning a parallel “GEO-only” stack. Google’s requirements apply to Google; other crawlers still need their own access tests. The Open Future Forum September 2026 report reports that 81% of 230 marketing and growth leaders were past exploring agentic AI. Attribution was the leading named challenge, appearing in 20 of 96 open answers. Those are different denominators, and neither measures scraper readiness. They support an operational purchase decision: require evidence trails alongside automation. In Eric’s September video, “A better model still needs a better workflow,” the discussion moves from model improvement to the workflow that makes the model useful. Applied to this page review, the sequence is concrete: inspect the fetch, locate the answer block, then repair the extraction contract. The scraper-specific example is our application of that lesson, not a claimed demonstration from the video. The engineer fetches the example service URL and finds a header, navigation menu, and application shell. The browser eventually displays the answer, but the tested non-rendering fetch does not. Asking a stronger model to summarize that response gives it more capability without supplying the missing text. Assign the fix to content engineering: deliver the essential answer in server-rendered or static HTML, give it a stable selector such as the explicit input contracts used for reliable marketing bots, and separate sections with meaningful headings. A selector such as #service-answer is a local extraction contract, not a universal crawler standard. The acceptance test is whether the chosen fetch method returns the answer and its qualifications together. Do not buy a stronger model to conceal missing schema or controls. First supply a stable content structure and an extraction test. Add relevant structured data only when it accurately describes visible content. No markup can substitute for an answer absent from the retrieved page. The technical SEO now checks the blocked money page from the opening review. The input bundle contains the URL, intended user agent, robots file, response headers, canonical element, and retrieved HTML. The checklist records which rule blocks access rather than collapsing every crawler into one permission. Google’s robots.txt documentation describes crawler access controls and their limitations. Robots rules are not authentication, and blocking crawling does not reliably remove a URL from search results. Keep private content behind access controls. For public content, distinguish crawl permission from indexing and snippet directives, and check the relevant crawler’s documentation. The filled artifact below uses illustrative URLs and proposed content. “Pass” means the stated local check succeeds, not that an LLM will cite the page. The proposed service claim must match the actual offer before it becomes production copy. LLM Scraper Readiness Checklist Canonicalization identifies the preferred URL; it does not compel a model’s citation choice. A useful chunk contains the claim, its scope, and supporting evidence without borrowing essential context from a distant accordion. For numerical claims, retain units, dates, denominators, and source links inside the same logical section. Run the checklist as a bounded job with a defined URL set and saved fetch evidence. Eric’s Jev middle-layer discussion recommends inexpensive classification, criteria checks, and routing. Apply that before drafting: classify each failure as access, extraction, evidence, or ownership, then send it to the responsible owner. Keep the human ship gate here. Eric’s early Jev work tests support routing uncertain classifications to review rather than automatic action. Name the technical SEO lead as approver for crawl changes and the content engineering lead for extraction changes; do not auto-publish either from a classifier’s output. Eric’s reusable-workflow recommendation changes the deliverable: build a job card the team can rerun after template changes, rather than commissioning a fresh premium audit each time. His decision-layer discussion adds a constraint: label thin evidence thin, and use misclassifications to improve checks before expanding automation. Single Brain is the AI implementation OS for job-specific agents, evaluation, and kill switches. Scope this installation around the checklist: a URL queue, saved fetches, deterministic access checks, extraction tests, evidence routing, and a stop control for unexpected behavior. The operator view should expose the retrieved passage and failure reason, not just a green status. Keep the work manual when permissions are unresolved, the page set changes rarely, or the business cannot define acceptable evidence. Automate repeatable checks once the inputs and acceptance criteria are stable. A stronger model remains the wrong purchase when the answer block, schema, or control layer is missing. Hire Single Grain to install and run the system when you need SEO, engineering, and content operations without staffing that coordination internally. Bring your money-page URLs, crawler policy, and representative templates to a Single Grain consultation. Scope the engagement around repaired retrieval paths and reusable operating jobs, with citation outcomes evaluated separately. Eric Siu is a seasoned entrepreneur and CEO of the digital marketing agency Single Grain, which drives scalable and predictable revenue growth using paid ads, SEO, and content marketing. He has successfully scaled multiple businesses and assisted clients in various industries, including Amazon, Uber, and Salesforce, to do the same. Eric hosts two podcasts: Marketing School with Neil Patel and Leveling Up, where he dissects growth levers that help businesses scale. Follow him on Twitter @ericosiu. Our newsletter is brimming with marketing strategies that are working right now and must-have resources. Join our community of 15,000+ subscribers, including professionals from Amazon, Google, and Samsung. Join 15,000+ marketers getting proven strategies Single Grain is a full-service digital marketing agency that helps great companies grow their revenues online. Get in touch: contact@singlegrain.com © 2026 Single Grain. All rights reserved. Sitemap | Privacy Policy | Personal Data Removal Request | Notice of Non-Affiliation | Accessibility Get Free Instant Access 8 Effective Online Marketing Tactics That Have Generated 1,545%+ ROI for our Customers (and You Can Easily Use) We hate SPAM and promise to keep your email address safe. Personal attention guaranteed You'll hear back from me or one of our senior strategists directly. "Single Grain was instrumental to our growth. They're especially ahead of the game with AI." — Yaniv Masjedi, Co-Founder & CMO, Nextiva Trusted by teams at Amazon, Uber, Salesforce, and Airbnb ClickFlow’s AI plans and writes production-grade content — so you don’t need 10 more writers and editors. Early adopters average 27% more organic traffic in 6 months. Karrot generates personalized ads and landing pages for every target account in minutes, not weeks. One team closed 2 deals from just 15 accounts in under 2 weeks.
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| Digit 5, l'umanoide Agility che lavora senza recinzioni | https://www.tecnoandroid.it/news/digit-… | 10 | Sep 26, 2026 16:00 | active | |
Digit 5, l'umanoide Agility che lavora senza recinzioniURL: https://www.tecnoandroid.it/news/digit-5-lumanoide-agility-che-lavora-senza-recinzioni-1977558/ Description: Il robot Digit 5 di Agility Robotics ha superato una valutazione indipendente OSHA e può lavorare accanto agli operai senza barriere. Content:
Con Digit 5 Agility Robotics prova a fare il salto che l’intera industria degli umanoidi aspetta da anni, cioè togliere le barriere fisiche che finora tenevano i robot separati dagli operai. L’azienda, fondata nel 2015 a Salem, in Oregon, sviluppa la famiglia Digit da diverse generazioni e sostiene che i modelli precedenti abbiano accumulato oltre 65.000 ore di lavoro effettivo presso clienti del calibro di GXO, Schaeffler, Amazon e Toyota Motor Manufacturing Canada. Tre anni di uso quotidiano del Digit 4 hanno prodotto una quantità di feedback che ora si ritrova, tradotta in hardware e software, nella nuova versione. Peggy Johnson, amministratrice delegata di Agility Robotics, la mette in termini piuttosto diretti, parlando della rimozione di un ostacolo fondamentale alla diffusione su larga scala degli umanoidi industriali. Il punto, insomma, non è tanto la forza bruta quanto la possibilità di lavorare accanto alle persone senza recinzioni. TecnoAndroid · su Google Seguici su Google e non perdere nulla Aggiungi TecnoAndroid alle tue fonti preferite su Discover e segui il nostro profilo Google: le notizie tech più importanti arrivano direttamente sul tuo telefono. L’architettura di sicurezza di Digit 5 si appoggia a tre dispositivi che lavorano insieme. Il robot combina algoritmi proprietari con una serie di sensori per capire se c’è qualcuno nei paraggi, poi decide da sé come comportarsi, scansando la persona, fermandosi oppure mettendosi a sedere. A questo si aggiungono segnali visivi e sonori che comunicano in anticipo l’intenzione di muoversi, mentre un controllore di sicurezza indipendente scatta nel momento in cui la distanza diventa pericolosa. Secondo l’azienda, Digit 5 è il primo robot umanoide a superare su una linea di produzione reale, quella di un cliente, una valutazione indipendente allineata agli standard di sicurezza industriale dell’Occupational Safety and Health Administration statunitense. C’è anche il capitolo NVIDIA: Agility Robotics è il primo partner di lancio di Halos for Robotics, la piattaforma di sicurezza appena presentata dal colosso dei chip, basata sull’infrastruttura IGX Thor e su Halos Core. Deepu Talla, vicepresidente della robotica e dell’AI embedded di NVIDIA, ha spiegato che la capacità di operare in totale sicurezza vicino a persone e beni è la condizione necessaria per passare dai prototipi ai dispiegamenti industriali veri. L’azienda dell’Oregon partecipa inoltre alla stesura degli standard ANSI/A3 TR R15.108 e ISO 25785 uno, ancora in elaborazione, dedicati ai robot mobili industriali e agli umanoidi. Sul fronte delle prestazioni i numeri sono concreti. Le gambe di Digit 5 sono progettate per sopportare sollevamenti ripetuti e il robot arriva a 22,7 kg, cioè il 40% in più rispetto a Digit 4. In un impianto che segue le regole OSHA, questo significa poter coprire da solo operazioni di sollevamento finora assegnate a un lavoratore umano. Cambia anche la batteria: 90 minuti di lavoro con una ricarica di nove minuti, quindi un rapporto tra autonomia e ricarica di dieci a uno, contro il due a uno della generazione precedente. Nella pratica il robot può restare operativo per più di venti ore su ventiquattro. Il perimetro dei compiti si allarga parecchio. Digit 4 si occupava soprattutto di movimentare contenitori, mentre Digit 5 è pensato per coprire a regime l’intero flusso di un impianto, dalla depallettizzazione della merce in arrivo all’alimentazione delle macchine, passando per confezionamento, sequenziamento e controllo qualità, fino alla pallettizzazione prima della spedizione. Grazie alla piattaforma Agility Arc, ogni trasferimento viene coordinato con robot mobili autonomi, nastri trasportatori e sistemi di gestione dello stabilimento, con monitoraggio di disponibilità, produttività e tempo medio tra gli incidenti. Un dato dal campo: nel sito GXO di Flowery Branch, Digit 4 ha superato i 100.000 sacchi movimentati con una precisione attorno al 98%. L’assemblaggio avviene a RoboFab, la fabbrica di Agility Robotics a Salem. Sono 6.500 metri quadrati che, a pieno regime, dovrebbero sfornare fino a 10.000 robot Digit all’anno, con un organico previsto superiore alle 500 persone. La novità geografica riguarda il mercato: per la prima volta Digit verrà venduto fuori da Stati Uniti e Canada, partendo da Unione europea e Regno Unito, dove però il modello deve ancora ottenere la certificazione CE e le altre omologazioni necessarie. L’accesso anticipato è atteso nel primo semestre del 2027, con la disponibilità generale annunciata per la fine del 2027 presso gli operatori di produzione, stoccaggio e distribuzione. Fonte: TecnoAndroid Notizie, recensioni e approfondimenti di tecnologia, scienza e innovazione. Non solo Android. Ultimo aggiornamento: 2026-09-26 17:50:00 2012 – 2026 © Tecnoandroid.it – Gestito dalla STARGATE SRLS – P.Iva: 15525681001 Testata telematica quotidiana registrata al Tribunale di Roma CON DECRETO N° 225/2015, editore STARGATE SRLS. Tutti i marchi riportati appartengono ai legittimi proprietari. Questo articolo potrebbe includere collegamenti affiliati: eventuali acquisti o ordini realizzati attraverso questi link contribuiranno a fornire una commissione al nostro sito. Non perderti nemmeno un’offerta Smartphone, notebook, gadget tech al prezzo più basso.
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| Toyota plans to deploy 400,000 factory robots from 2028 | … | https://www.automotiveworld.com/news/to… | 4 | Sep 26, 2026 00:01 | active | |
Toyota plans to deploy 400,000 factory robots from 2028 | Automotive WorldURL: https://www.automotiveworld.com/news/toyota-plans-to-deploy-400000-factory-robots-from-2028/ Description: Having robots learn by watching workers lets Toyota generate training data organically rather than depend on simulation. By Stewart Burnett Content:
Don't have an account? Subscribe “Our goal is to help stakeholders understand the future of mobility.” Home › News › Toyota plans to deploy 400,000 factory robots from 2028 Having robots learn by watching workers lets Toyota generate training data organically rather than depend on simulation. By Stewart Burnett Toyota estimates it could spend as much as JP¥1tn (US$6.4bn) annually from 2028 automating its global factories, deploying roughly 400,000 robots at both its own plants and those of major suppliers. The automaker has told investors that the figure covers both humanoid and non-humanoid machines, including replacements for existing equipment, although it neither confirmed the investment would definitely proceed nor how many years it might continue. Of the total, Toyota plans to deploy 150,000 robots at its own plants and 250,000 across group company facilities. The flagship humanoid, named Eley, weighs 50 kg, uses two-fingered hands rather than a full humanoid grip, and moves on wheels powered by battery or cord instead of walking. This is a deliberate trade-off toward proven reliability over the bipedal approach rivals like Tesla and Figure are pursuing for their own factory robots. Eley learns by observing Toyota’s own workers rather than relying primarily on external datasets or simulation: employees wear finger-shaped jigs modelled on the robot’s hands while repeating routine tasks, letting Eley learn its technique directly from demonstration. At a technology briefing at Toyota’s European headquarters earlier in September, the robot folded T-shirts with near-perfect accuracy after 1,500 practice sessions over two weeks. Toyota eventually plans to share centrally learned data across its factories worldwide, so robots in different countries can acquire the same skills without repeating the training process locally. However, this rollout leans on infrastructure Toyota has built over decades, and may therefore be outdated, rather than starting from a blank slate. As it stands, the group operates 60 factories globally and employs 18,000 veteran “takumi” workers, whose expertise it intends to capture as training data, and plans call for robots to help train new human employees as well as learn from existing ones. “We aim for a world where robots coexist with humans, rather than replacing them,” said Executive Vice President Hiroki Nakajima in a statement. Critics might argue that the operative word there is “aim”. While not catching the headlines of Tesla’s Optimus or Hyundai’s Atlas humanoid robots, Toyota’s presence in this segment is nothing new. Indeed, it stretches back more than two decades, from the Human Support Robot developed for elder care to the Welwalk rehabilitation exoskeleton and the teleoperated T-HR3 humanoid. More recently, however, it has shifted towards Large Behavior Models developed through the Toyota Research Institute, which use diffusion-based generative AI to teach robots physical skills, such as pouring liquid or using a tool, from just a few minutes of human demonstration rather than manually coded instructions. The automaker has separately partnered with Agility Robotics to deploy bipedal Digit humanoids for tote handling and logistics tasks at sites including Toyota Motor Manufacturing Canada. Toyota’s wheeled, multi-form-factor approach sits apart from rivals converging on a single bipedal design, and the distinction matters most in how each company is choosing to compete. Hyundai’s Boston Dynamics-built Atlas, due at its Georgia plant from 2028, and Tesla’s Optimus both bet on human-like mobility as the eventual differentiator. Meanwhile, Toyota is betting instead that a robot’s ability to learn fine manual skills quickly, on wheels, from watching its own workforce, matters more for near-term factory deployment. September 25, 2026 September 25, 2026 September 25, 2026 Let us help you understand the future of mobility "*" indicates required fields Your essential guide to the automotive industry developments that matter. News every Monday. Analysis every Thursday. Δ August 7, 2025 Q2 2026 was one of the industry's best periods in years, yet there's a disconnect between revenue, profit and volume. July 31, 2026 Automotive World's monthly snapshot of past and future sales LV volumes broken down by region. July 24, 2026 Automotive World forecasts production output for Mitsubishi during the period to 2030. Welcome back , to continue browsing the site, please click here
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| Unitree humanoid robots return to America’s Got Talent for Chinese … | https://interestingengineering.com/ai-r… | 10 | Sep 25, 2026 08:00 | active | |
Unitree humanoid robots return to America’s Got Talent for Chinese dance finaleDescription: Chinese dancer Wu Yufei performs traditional shuixiu water-sleeve routine with 8 Unitree humanoid robots in the America’s Got Talent finale. Content:
Pick the engineering stories that matter and get them in your inbox. Access expert insights, exclusive content, and a deeper dive into engineering and innovation all with fewer ads or a completely ad-free experience. All Rights Reserved, IE Media, Inc. Follow Us On Access expert insights, exclusive content, and a deeper dive into engineering and innovation all with fewer ads or a completely ad-free experience. All Rights Reserved, IE Media, Inc. Chinese dancer Wu Yufei and eight Unitree humanoids have taken traditional Chinese dance to one of America’s biggest television stages. A Chinese dancer and eight humanoid robots from Unitree Robotics have brought a centuries-old Chinese dance tradition to the stage of America’s Got Talent, performing a synchronized shuixiu, or “water-sleeve,” routine during the Season 21 finale. The performance by 27-year-old Sichuan dancer Wu Yufei and the Unitree G1 robots combined traditional Chinese movement with humanoid robotics, giving American television audiences an unusual meeting of classical performance and modern machines. The group had already become one of the season’s most recognizable technology-focused acts before reaching the finale. Wu and eight Unitree G1 humanoids first appeared on America’s Got Talent during the June 2 auditions. Their tightly synchronized routine included dance, martial-arts movements and coordinated backflips, earning all four judges’ approval and sending the act through to the next stage. The robots returned during the competition before advancing to the September 22 finale. Reporting from Chinese media described the latest performance as a water-sleeve dance, incorporating the long, flowing sleeves associated with Chinese traditional theater and dance. The act performed under the name Homies, with Wu serving as the human dancer alongside the robot performers. Chinese reporting said Unitree chief marketing officer Wang Qixin described the group as the first Chinese team to reach the America’s Got Talent finale. The finale performance also builds on a longer effort to make humanoid robots capable of performing complicated choreography alongside humans. Getting a humanoid robot to reproduce a dance routine requires considerably more than simply telling it to imitate a human. According to reporting by AFP, the process involves capturing detailed human movements, simulating them on computers and transferring the resulting motion data to the robots. Engineers then have to fine-tune the movements so the machines can execute the choreography reliably. That creates a particular challenge for dance. Robots can be highly consistent once a sequence has been programmed, but they have difficulty with movements requiring the flexibility of a human body. They also generally cannot improvise or adjust to unexpected changes in music the way a human dancer can. For the Unitree performance, however, that repeatability is an advantage. Eight robots can be programmed with the same movements and execute them in synchronization, allowing choreography that would otherwise require considerable coordination between individual performers. Wu told AFP that after months of training with the robots, they began to feel less like machines and more like members of a team. The G1 is Unitree’s humanoid robot platform and has become increasingly visible outside conventional robotics demonstrations. The machines have been shown performing complex movements including walking, balancing, martial arts and backflips. Unitree robots also gained widespread attention in China after appearing in the country’s annual Spring Festival Gala, where humanoid machines performed alongside human dancers. That exposure helped fuel a wider trend of robot performances at commercial events and public shows. For the robotics industry, performances such as the America’s Got Talent routine serve a purpose beyond entertainment. They provide a highly visible demonstration of balance, motion control, multi-robot coordination and the ability to repeatedly execute complex sequences in a public environment. At the same time, the performance highlights the current limits of humanoid robotics. The choreography remains heavily scripted, with humans and engineers playing a central role in designing and refining the movements. Still, putting eight humanoid robots alongside a human dancer and having them perform a traditional Chinese dance on one of America’s biggest talent shows offers a striking demonstration of how rapidly robotics is moving from laboratories and demonstrations into mainstream entertainment. Kaif Shaikh is a journalist and writer passionate about turning complex information into clear, impactful stories. His writing covers technology, sustainability, geopolitics, and occasionally fiction. A graduate in Journalism and Mass Communication, his work has appeared in the Times of India and beyond. After a near-fatal experience, Kaif began seeing both stories and silences differently. Outside work, he juggles far too many projects and passions, but always makes time to read, reflect, and hold onto the thread of wonder. Premium Follow
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| Figure AI humanoids sort 28,000 packages in 24-hour autonomous test | https://interestingengineering.com/ai-r… | 10 | Sep 24, 2026 16:00 | active | |
Figure AI humanoids sort 28,000 packages in 24-hour autonomous testURL: https://interestingengineering.com/ai-robotics/figure-ai-humanoids-24-hour-autonomous-run Description: Figure AI says its humanoid robots completed over 24 hours of nonstop autonomous work using Helix-02 AI. Content:
Pick the engineering stories that matter and get them in your inbox. Access expert insights, exclusive content, and a deeper dive into engineering and innovation all with fewer ads or a completely ad-free experience. All Rights Reserved, IE Media, Inc. Follow Us On Access expert insights, exclusive content, and a deeper dive into engineering and innovation all with fewer ads or a completely ad-free experience. All Rights Reserved, IE Media, Inc. Figure AI claims its humanoid robots crossed 24 hours of nonstop autonomous package sorting without failures. Figure AI says its humanoid robots have now crossed 24 hours of continuous autonomous work, extending what was initially planned as an eight-hour test into a nonstop multi-day operation. The California-based robotics startup said three humanoid robots running its Helix-02 AI system are autonomously sorting small packages around the clock without human control. The company livestreamed the operation online, where the robots were nicknamed Bob, Frank, and Gary by viewers. “Our original goal was an 8-hour run. After zero failures yesterday, we decided to keep going. We’re now over 24 hours of continuous autonomous operation without a failure. This is uncharted territory,” Brett Adcock, founder and CEO of Figure AI, wrote on X. The company said the robots have already sorted more than 28,000 packages during the ongoing operation while maintaining speeds close to human workers. Day 2 is Live: Watch humanoid robots Bob, Frank, and Gary running 24/7. This is fully autonomous running Helix-02 https://t.co/zaRVkoLa4e The livestream has drawn significant online attention, with viewers continuously tracking the robots’ uptime and performance as the operation moved beyond its original eight-hour target. Figure AI also added visible name tags to the robots after commenters began referring to them as Bob, Frank, and Gary. According to Figure AI, the robots detect barcodes, pick up packages, and place them barcode face-down onto conveyor belts using onboard cameras and AI reasoning. “Humans average around 3 seconds per package. F.03 is now around human parity. The robots are reasoning directly from camera pixels,” Adcock said. Frank merch in the house!!p.s. robots are closing in on 30 hours of nonstop operations. Hopefully they don't fail and keep going pic.twitter.com/LXExFik5bo The company added that the humanoids are operating fully autonomously using Helix-02, its in-house neural network running entirely onboard the robots. Figure AI stressed there is no teleoperation involved in the process. “There is no teleoperation – every action comes directly from Helix-02,” Adcock wrote. The system also includes automatic recovery mechanisms. Figure AI said if a robot gets stuck or encounters an unfamiliar situation, the AI system can trigger an autonomous reset and resume work without human intervention. “If the robot gets stuck or the AI policy goes out of distribution, Helix triggers an automatic reset,” Adcock said. Figure AI further claimed the robots can independently leave the work floor for maintenance if software or hardware issues appear, while another robot automatically takes over operations to maintain uptime. “If a robot has a software or hardware issue, it autonomously leaves for maintenance and another robot takes over,” Adcock wrote. The latest demonstration builds on Figure AI’s earlier claims that its humanoid robots completed full eight-hour shifts autonomously using Helix-02. The company has also previously tested humanoid robots at BMW manufacturing facilities in South Carolina. Helix-02 is designed as a unified neural network combining vision, touch sensing, proprioception, and whole-body control. Unlike conventional industrial robots that separate movement and manipulation systems, Figure AI says its robots use a single AI model to handle walking, balancing, object handling, and coordination in dynamic environments. The company is competing with firms including Tesla, Agility Robotics, and Apptronik to commercialize humanoid robots for warehouse, factory, and logistics operations. With over a decade-long career in journalism, Neetika Walter has worked with The Economic Times, ANI, and Hindustan Times, covering politics, business, technology, and the clean energy sector. Passionate about contemporary culture, books, poetry, and storytelling, she brings depth and insight to her writing. When she isn’t chasing stories, she’s likely lost in a book or enjoying the company of her dogs. Premium Follow
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| Figure, accordo con Nscale per 100.000 GPU e robot umanoidi … | https://www.tecnoandroid.it/news/figure… | 10 | Sep 24, 2026 16:00 | active | |
Figure, accordo con Nscale per 100.000 GPU e robot umanoidi - TecnoAndroidDescription: Un'intesa con Nscale porta fino a 100.000 GPU NVIDIA all'addestramento dei Figure robot umanoidi, con primo dispiegamento in Texas nel 2027. Content:
Un accordo destinato a spostare gli equilibri nella robotica: Figure, tra le realtà più osservate del settore negli Stati Uniti, si è assicurata una capacità di calcolo enorme per addestrare i modelli di intelligenza artificiale che governeranno i suoi robot umanoidi di prossima generazione. Non un aggiornamento marginale, ma la costruzione di quella che si annuncia come una delle infrastrutture di calcolo più grandi mai dedicate all’intelligenza artificiale applicata al mondo fisico. Il numero che colpisce è chiaro e difficile da ignorare: fino a 100.000 GPU. La partnership è stata siglata con la britannica Nscale, e prevede l’utilizzo della futura piattaforma NVIDIA Vera Rubin. Il primo dispiegamento è programmato per la seconda metà del 2027, con base in Texas. Una tempistica che dice molto sulla natura del progetto, perché parlare di un’infrastruttura di queste dimensioni significa ragionare su anni, non su mesi, e significa mettere in fila fornitori, energia, spazi fisici e software. TecnoAndroid · su Google Seguici su Google e non perdere nulla Aggiungi TecnoAndroid alle tue fonti preferite su Discover e segui il nostro profilo Google: le notizie tech più importanti arrivano direttamente sul tuo telefono. La domanda sorge spontanea per chi non frequenta il settore ogni giorno. Un umanoide non ha bisogno soltanto di motori e sensori, ha bisogno di capire cosa sta guardando e decidere cosa fare. È qui che entra in gioco Helix, il sistema di intelligenza artificiale sviluppato da Figure che permette ai suoi robot di interpretare le immagini raccolte e tradurle in movimenti concreti, in azioni utili. Prendere un oggetto, spostarlo, riconoscere una situazione mai vista prima e reagire senza istruzioni scritte riga per riga. Il punto è che un sistema di questo tipo migliora soltanto se gli vengono mostrati esempi. Molti esempi. E crescenti. Non simulazioni astratte, ma situazioni tratte dal mondo reale, con tutte le sue imperfezioni, le luci sbagliate, gli oggetti fuori posto, le variabili che nessun laboratorio riesce a prevedere completamente. Ogni volta che la quantità di dati aumenta, aumenta anche la potenza di calcolo necessaria per digerirli e trasformarli in comportamenti affidabili. Ecco spiegato il motivo di una corsa alle GPU che, vista da fuori, potrebbe sembrare sproporzionata. Quello che sta emergendo è una differenza sostanziale rispetto all’intelligenza artificiale a cui il pubblico si è abituato negli ultimi anni. I modelli che generano testo o immagini lavorano dentro uno schermo, dove un errore si corregge con un clic. La AI fisica invece agisce nello spazio, tra persone e oggetti reali, e un errore ha conseguenze immediate. Questo alza l’asticella in modo netto, sia sulla qualità dei dati sia sulla mole di addestramento richiesta. Figure ha scelto di affrontare il problema alla radice, costruendo la propria capacità di calcolo su misura invece di dipendere soltanto da risorse condivise. Una strategia impegnativa dal punto di vista industriale, che però risponde a una logica semplice: chi controlla l’infrastruttura controlla i tempi di sviluppo. E in un settore dove la competizione si misura in mesi di vantaggio, il fattore tempo pesa quanto la tecnologia. Il calendario, per ora, resta quello annunciato. Le prime GPU basate su NVIDIA Vera Rubin arriveranno nella seconda metà del 2027 sul suolo texano, dando forma concreta a un progetto che punta a rendere i robot umanoidi di Figure più autonomi e più capaci di operare in ambienti non preparati appositamente per loro. Nel frattempo lo sviluppo di Helix continua, alimentato dagli esempi raccolti sul campo, perché senza quel materiale nessuna quantità di silicio produrrebbe risultati. Fonte: TecnoAndroid Notizie, recensioni e approfondimenti di tecnologia, scienza e innovazione. Non solo Android. Ultimo aggiornamento: 2026-09-24 17:50:00 2012 – 2026 © Tecnoandroid.it – Gestito dalla STARGATE SRLS – P.Iva: 15525681001 Testata telematica quotidiana registrata al Tribunale di Roma CON DECRETO N° 225/2015, editore STARGATE SRLS. Tutti i marchi riportati appartengono ai legittimi proprietari. Questo articolo potrebbe includere collegamenti affiliati: eventuali acquisti o ordini realizzati attraverso questi link contribuiranno a fornire una commissione al nostro sito. Non perderti nemmeno un’offerta Smartphone, notebook, gadget tech al prezzo più basso.
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| China is exploring humanoid robots for war – but what … | https://theconversation.com/china-is-ex… | 6 | Sep 23, 2026 00:03 | active | |
China is exploring humanoid robots for war – but what role could they play?Description: The People’s Liberation Army called for researchers to accelerate progress on building humanoid robots designed for military uses. Content:
Share article Print article At the World Humanoid Robot Games in Beijing, a humanoid robot named Tiangong Ultra ran the 100-metre sprint in a staggering 8.64 seconds, shattering Jamaican sprinter Usain Bolt’s legendary human world record of 9.58 seconds. The internet laughed at the robots’ awkward, lurching finishes and spectacular falls – but the laughter masked a chilling reality. Just two days after the games concluded the official newspaper of China’s People’s Liberation Army (PLA) called for researchers to accelerate moving these cutting-edge machines from the laboratory to military training grounds. They referred to them not as experiments, but as “combatants”. As a robotics researcher myself, working daily with robot simulation, reinforcement learning, and the foundational software and simulation tools that power them (such as ROS 2 and Gazebo), I watch these developments with a mix of awe and deep concern. The public often views humanoid robots as clunky sci-fi novelties. The reality is far different: the hardware is already highly capable, and the software is advancing at an unprecedented pace. But the real story is not just that a robot can beat a human on a running track. It’s what that performance reveals: China’s ability to integrate motors, reducers (gears used in precise joint movement), sensors, control software, testing infrastructure and manufacturing capacity into one unified industrial system. A sprinting humanoid is not just a stunt. At high speeds, every foot strike creates violent ground reaction forces. Balance corrections must happen in milliseconds, as a single small error can send the machine crashing into the barrier. For decades, the physical mechanics of humanoid robots, including the actuators (a component, such as a motor, that converts energy into physical movement), sensors, and the sheer physics of bipedal balance, were the main hurdles. Today, those mechanical problems are largely solved. Carbon fibre and aluminium bodies keep mass and inertia (how a robot’s mass resists change) low. Advancements in actuator technology deliver torque capacities of up to 400Nm (Newton-metres – a unit of torque, or twisting force) in humanoid joints, providing the ultimate combination of power and agility. What you saw on the track in Beijing was not just a triumph of motors, but a triumph of coding. A real breakthrough is happening in software. Robots are trained in virtual simulation environments, running millions of trial-and-error scenarios through reinforcement learning before the robot ever takes a physical step. Reinforcement learning is an area of artificial intelligence (AI) where robots make decisions based on the results of their actions. This allows them to learn how to recover from trips, adjust to uneven ground, and process chaotic environments in real time. Humanoid robots are rapidly bridging the gap between controlled laboratory conditions and the unpredictable real world. But all this leads to an inevitable question: why would a military want a complex, expensive humanoid when they could use vastly cheaper, rugged, tracked or wheeled drones? In open-field combat, tracked vehicles are absolutely superior. They heave bigger payloads, carry thick ballistic armour, and are far more energy efficient. However, the nature of conflict is changing – and urban environments play an increasing role in military thinking. Cities are built exclusively for humans. A tracked robot cannot easily climb a vertical fire escape ladder, turn a standard door handle, squeeze through a narrow, debris filled stairwell, or sit in the driver’s seat of a captured supply truck. A humanoid robot acts as a “drop-in replacement” for a human soldier. If a building is designed for a human to navigate, a humanoid robot can navigate it without requiring custom redesigns or specialised ramps. This brings us to a profound ethical crossroads. Many of us in the robotics field do not endorse offensive warfare. But these machines have an undeniable utility in defensive scenarios and those concerned with neutralising threats to military personnel and civilians. Sending a humanoid into a building to rescue hostages, neutralising an active threat such as hostage-takers, or clearing a booby trapped room saves human lives. In fact, the modern surge in humanoid robotics was largely kickstarted by the US government’s Darpa Robotics Challenge, which funded bipedal robots specifically to respond to disasters such as the Fukushima nuclear meltdown where human responders could not survive. The dilemma is that the technology is agnostic to intent. The baseline capabilities required to navigate a ruined building and extract a casualty are the same capabilities needed to enter a building and kill enemy soldiers. If the technology is ready for defence, it is also ready for offensive use. Perhaps the most alarming aspect of this rapid advancement is how accessible it is. Unlike nuclear technology or stealth aircraft, modern robotics thrives on open-source frameworks. For example, a military specific software ecosystem (a network of apps and other services that work together) known as ROS-M, along with simulation tools and training datasets, are largely public and shared across global academic communities. With enough skill, a dedicated adversary can replicate advanced robotic behaviour with relative ease. We can no longer afford to treat humanoid robotics purely as an academic pursuit or a commercial novelty. We need an urgent international conversation about how to control these advances. Just as we regulate the export of certain microchips and aerospace components, we must begin protecting the software architecture and training pipelines that give these machines their minds. The hardware is walking out of the lab – it is time our policies caught up. Senior Lecturer, Department of Engineering, Nottingham Trent University Kartikeya Walia does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment. Nottingham Trent University provides funding as a member of The Conversation UK. View all partners https://doi.org/10.64628/AB.6dpetxtxx Copyright © 2010–2026, The Conversation Media Group Ltd
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| Modelado y control de robots aéreos bioinspirados con técnicas de … | https://oa.upm.es/97915/ | 10 | Sep 23, 2026 00:03 | active | |
Modelado y control de robots aéreos bioinspirados con técnicas de aprendizaje por refuerzo | Archivo Digital UPMContent:
Los robots aéreos bioinspirados, capaces de reproducir estrategias de vuelo observadas en aves e insectos, ofrecen ventajas potenciales frente a los UAV convencionales en maniobras agresivas como el posado (perching). En este tipo de maniobras, el vehículo debe aproximarse a un objetivo y disipar gran parte de su energía cinética en una distancia muy reducida. El control de estos sistemas resulta especialmente complejo debido a su dinámica no lineal, al fuerte acoplamiento entre traslación y rotación, y a la variabilidad aerodinámica introducida por la morfología variable de sus alas y cola. Las técnicas clásicas de control, generalmente basadas en modelos linealizados, presentan limitaciones importantes en estas condiciones, lo que motiva el estudio de enfoques alternativos basados en aprendizaje por refuerzo. Este Trabajo Fin de Máster tiene como objetivo desarrollar y evaluar un entorno de modelado y control para un robot aéreo bioinspirado, aplicando técnicas de aprendizaje por refuerzo profundo para estudiar su capacidad de ejecutar maniobras de aproximación y posado. El trabajo parte del estudio de referencia de Wüest et al., adoptando una formulación longitudinal bidimensional simplificada que permite centrar el análisis en las variables principales de la maniobra: posición, velocidad, actitud de cabeceo y ángulo de ataque. Para ello, se implementó un entorno de simulación compatible con Gymnasium y Stable-Baselines3, en el que se definieron el espacio de observación, el espacio de acciones, la función de recompensa y las condiciones de terminación del episodio. Sobre este entorno se entrenaron y compararon dos algoritmos de aprendizaje por refuerzo: Proximal Policy Optimization (PPO) y Soft Actor-Critic (SAC). Además, se analizó la influencia de distintos márgenes de éxito sobre el comportamiento de control aprendido. Los resultados muestran que ambos algoritmos son capaces de aprender políticas de control exitosas para la tarea de posado, generando comportamientos físicamente coherentes: reducción de la velocidad de avance, aumento del ángulo de cabeceo y del ángulo de ataque en la fase final, y coordinación entre el empuje, el elevador y la morfología variable del ala y la cola. PPO ofrece un rendimiento sólido y estable para márgenes de éxito de referencia e intermedios, mientras que SAC demuestra mayor robustez y genera trayectorias más suaves y repetibles bajo condiciones terminales más estrictas. No obstante, se identifica como principal limitación que la velocidad final del vehículo se mantiene en torno a 4 m/s en la mayoría de los casos, por lo que la maniobra aprendida corresponde a una aproximación controlada y no a una detención completa en el punto de posado. En conjunto, este trabajo demuestra que el aprendizaje por refuerzo profundo constituye un marco válido y flexible para el estudio de estrategias de aproximación y posado en robots aéreos bioinspirados. Asimismo, sienta las bases para futuras extensiones hacia modelos tridimensionales más realistas, funciones de recompensa más avanzadas y una posible transferencia a plataformas reales. --ABSTRACT-- Bio-inspired aerial robots, which reproduce flight strategies observed in birds and insects, offer potential advantages over conventional UAVs in aggressive maneuvers such as perching. In these maneuvers, the vehicle must approach a target while dissipating a large part of its kinetic energy over a very short distance. Controlling these systems is particularly challenging due to their nonlinear dynamics, the strong coupling between translational and rotational motion, and the aerodynamic variability introduced by the variable morphology of their wings and tail. Classical control techniques, typically based on linearized models, present important limitations under these conditions, which motivates the study of alternative approaches based on reinforcement learning. The objective of this Master’s Thesis is to develop and evaluate a modeling and control environment for a bio-inspired aerial robot, applying deep reinforcement learning techniques to study its ability to perform approach and perching maneuvers. The work is based on the reference study by Wüest et al., adopting a simplified twodimensional longitudinal formulation that allows the analysis to focus on the main variables involved in the maneuver: position, velocity, pitch attitude, and angle of attack. To this end, a simulation environment compatible with Gymnasium and StableBaselines3 was implemented, defining the observation space, action space, reward function, and episode termination conditions. Two reinforcement learning algorithms were trained and compared in this environment: Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC). In addition, the influence of different success margins on the learned control behavior was analyzed. The results show that both algorithms are able to learn successful control policies for the perching task, generating physically coherent behaviors: reduction of forward velocity, increase in pitch angle and angle of attack during the final phase, and coordination between thrust, elevator deflection, and the variable morphology of the wing and tail. PPO provides solid and stable performance for the reference and intermediate success margins, whereas SAC shows greater robustness and produces smoother and more repeatable trajectories under stricter terminal conditions. However, the main limitation identified is that the final vehicle velocity remains around 4 m/s in most cases, meaning that the learned maneuver corresponds to a controlled approach rather than a complete stop at the perch. Overall, this work demonstrates that deep reinforcement learning provides a valid and flexible framework for studying approach and perching strategies in bio-inspired aerial robots. It also establishes a foundation for future extensions toward more realistic three-dimensional models, improved reward functions, and possible transfer to real robotic platforms. El Archivo Digital UPM es el repositorio digital institucional mantenido por la Biblioteca de la Universidad Politécnica de Madrid. Desarrollado y gestionado con EPrints. Sindicación: Atom, RSS 2.0 y RSS 1.0 (HTML) Recolección: OAI 2.0 El Archivo Digital UPM es el repositorio digital institucional mantenido por la Biblioteca de la Universidad Politécnica de Madrid. Desarrollado y gestionado con EPrints. Sindicación: Atom, RSS 2.0 y RSS 1.0 (HTML) Recolección: OAI 2.0
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| humanoid robots sales: Humanoid robot sales tally hit 7,000 globally … | https://economictimes.indiatimes.com/te… | 10 | Sep 22, 2026 16:00 | active | |
humanoid robots sales: Humanoid robot sales tally hit 7,000 globally last year - The Economic TimesDescription: Around 7,000 humanoid robots were sold worldwide last year for industrial and professional service use, figures compiled by the International Federation of Robotics showed. Content:
Listen to this article in summarized format (Catch all the Technology News News, and Latest News Updates on The Economic Times.) ...more Popular Categories Hot on Web In Case you missed it Top Searched Companies Other useful Links Top Calculators Top Slideshow Top Story Listing Top Prime Articles Top Definitions Top Commodities Private Companies Top Market Pages Latest News follow us on Download ET App:
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| China's robots move from prototypes to mass deployment | http://www.ecns.cn/cns-wire/2026-07-29/… | 10 | Sep 22, 2026 08:00 | active | |
China's robots move from prototypes to mass deploymentURL: http://www.ecns.cn/cns-wire/2026-07-29/detail-ihfhvays7502149.shtml Content:
(ECNS) -- China's robotics industry is moving beyond trade exhibition showcases into large-scale commercial deployment, as robots are increasingly being used in factories, retail stores and elderly care facilities across the country. According to the Ministry of Industry and Information Technology (MIIT), in the first half of 2026, Chinese-developed quadruped robots accounted for nearly 70% of global sales, while more than 400 humanoid robot models had been launched, representing over half of the world's total. Wang Weiming, chief engineer of the MIIT, said that under the "Robot+" initiative, inspection robots are now operating in underground mines and high-voltage tunnels, while industrial robots have been deployed on automotive welding lines at scale, gradually replacing workers in high-temperature, toxic, high-altitude and other hazardous environments. The 2026 World Artificial Intelligence Conference (WAIC), held in Shanghai in July, offered a glimpse into how China's robotics industry is transitioning from laboratory breakthroughs to practical applications. At the booth of Lejoin Intelligence, three mass-produced robots continuously performed real-world tasksâincluding depalletizing cartons, handling plastic crates and loading small componentsâfor hours, showing they are ready for factory deployment. Another industry leader, Mech-Mind Robotics, unveiled its "One Brain, Multiple Forms" embodied AI system. Powered by the company's self-developed Mech-GPT multimodal foundation model, the platform supports humanoid, industrial and mobile robots, enabling workflows ranging from multi-robot industrial collaboration to retail order fulfillment. Beyond factories, robots are increasingly entering everyday life. At a smart elderly care station in Beijing's Yizhuang region, more than 40 intelligent robots operate throughout a four-story, 1,100-square-meter facility, providing services ranging from cooking and meal delivery to daily care assistance. The center welcomes more than 300 elderly visitors each day and also serves as a testing ground for improving robotic technologies.. Robots are also finding their place in retail. At a FamilyMart convenience store in Beijing, customers can simply ask for a grilled sausage. A robotic store assistant smoothly turns, extends its robotic arm toward the grill, precisely picks up a freshly cooked sausage and places it neatly on a serving tray. From factory workshops to elderly care centers and neighborhood convenience stores, robots are rapidly expanding beyond industrial production into everyday services. As embodied AI matures and commercialization accelerates, the vision of humans and robots working side by side is no longer a distant aspirationâit is steadily becoming reality. (By Gong Weiwei) China's delivery robots impress Moroccan visitor 'AI won't turn into monster robots,' says AI governance expert More than half of world's humanoid robots produced by China
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| Fourth Forum on China-ASEAN Artificial Intelligence Cooperation Successfully Held in … | https://www.manilatimes.net/2026/09/20/… | 0 | Sep 22, 2026 00:01 | active | |
Fourth Forum on China-ASEAN Artificial Intelligence Cooperation Successfully Held in Nanning, Drawing Over 700 DelegatesDescription: NANNING, CHINA - Media OutReach Newswire - 20 September 2026 - On September 16, as an international science and technology exchange platform included in the lis... Content: |
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| Agility svela Digit 5: il robot per l'industria arriva in … | https://www.punto-informatico.it/agilit… | 10 | Sep 21, 2026 16:00 | active | |
Agility svela Digit 5: il robot per l'industria arriva in EuropaURL: https://www.punto-informatico.it/agility-svela-digit-5-robot-25-miliardi-euro-arriva-europa/ Description: Agility Robotics ha svelato Digit 5, il nuovo robot umanoide destinato al lavoro industriale su larga scala. Content:
Fondata nel 2015 a Salem, nell’Oregon, Agility Robotics sviluppa Digit da diverse generazioni. Secondo l’azienda, i modelli precedenti hanno accumulato oltre 65.000 ore di operatività presso clienti quali GXO, Schaeffler, Amazon e Toyota Motor Manufacturing Canada. Dopo tre anni di utilizzo del Digit 4 da parte di questi clienti, Digit 5 ne raccoglie i riscontri e rimuove le barriere fisiche che fino ad ora separavano il robot dai dipendenti. Il Digit 5 elimina un ostacolo fondamentale al dispiegamento su larga scala dei robot umanoidi in ambiente industriale, ha sintetizzato Peggy Johnson, CEO di Agility Robotics. La nuova architettura di sicurezza di Digit 5 si articola in tre dispositivi. Per individuare una persona nelle vicinanze, il robot combina algoritmi proprietari con diversi sensori, quindi reagisce in modo autonomo evitandola, arrestandosi o assumendo una posizione seduta. In aggiunta, segnali visivi e sonori avvertono le persone presenti della sua intenzione di movimento, e un controllore di sicurezza indipendente attiva la risposta appropriata non appena una persona si trova a una distanza pericolosa. Secondo Agility Robotics, Digit 5 è il primo robot umanoide ad aver superato con successo, su una linea di produzione di un cliente, una valutazione indipendente conforme agli standard di sicurezza industriale dell’Occupational Safety and Health Administration (OSHA) statunitense. L’azienda è anche il primo partner di lancio di Halos for Robotics, la piattaforma di sicurezza appena presentata da NVIDIA, con la sua infrastruttura IGX Thor e Halos Core. La capacità di operare in totale sicurezza in prossimità delle persone e dei beni è essenziale per consentire ai robot umanoidi di passare dalla fase di prototipi a quella di dispiegamenti industriali su larga scala, ha spiegato Deepu Talla, vicepresidente della robotica e dell’AI embedded di NVIDIA. Agility Robotics contribuisce infine agli standard ANSI/A3 TR R15.108 e ISO 25785-1, in fase di elaborazione per i robot mobili industriali e gli umanoidi. Le gambe di Digit 5 possono resistere ai sollevamenti ripetuti. Solleva fino a 22,7 kg in modo ripetuto, ovvero il 40% in più rispetto a Digit 4. In un impianto regolamentato dall’OSHA, Digit 5 può coprire da solo le operazioni di sollevamento finora affidate a un operatore umano. Digit 5 integra inoltre una nuova batteria, con 90 minuti di autonomia per una ricarica di nove minuti. Di conseguenza, il rapporto autonomia-ricarica di Digit 5 è di 10 a 1, contro 2 a 1 per Digit 4, e il robot può funzionare per oltre venti ore su una giornata di ventiquattro ore. Digit 4 ha gestito principalmente la movimentazione di contenitori. Digit 5 dovrà coprire, a regime, l’intero flusso di un impianto. Il robot depallettizza le merci al loro arrivo, alimenta le macchine, partecipa al confezionamento e al sequenziamento, controlla la qualità degli articoli lungo tutto il loro percorso, quindi pallettizza i prodotti prima della spedizione. Grazie alla piattaforma Agility Arc, Digit coordina anche ogni trasferimento con i robot mobili autonomi, i nastri trasportatori e i sistemi di gestione dell’impianto, con monitoraggio della disponibilità, della produttività e del tempo medio tra gli incidenti. Presso GXO, nel sito di Flowery Branch, Digit 4 ha superato la soglia dei 100.000 sacchi movimentati con una precisione di circa il 98%, secondo Agility Robotics. Digit viene assemblato presso RoboFab, lo stabilimento di Agility Robotics a Salem, nell’Oregon. A pieno regime, questo stabilimento di 6.500 m² è in grado di produrre fino a 10.000 robot Digit all’anno, con un organico previsto di oltre 500 persone. Agility Robotics prevede di vendere Digit per la prima volta al di fuori degli Stati Uniti e del Canada, a partire dall’Unione europea e dal Regno Unito, dove Digit 5 deve ancora ottenere la certificazione CE e le altre omologazioni necessarie. Un accesso anticipato è previsto a partire dal primo semestre 2027, prima di una disponibilità generale annunciata per la fine del 2027 presso gli operatori di produzione, stoccaggio e distribuzione. Tiziana Foglio Pubblicato il 17 set 2026
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| Agility Digit 5 tanıtıldı: İnsanları algıladığında çömelecek | DonanımHaber | https://www.donanimhaber.com/agility-di… | 10 | Sep 21, 2026 16:00 | active | |
Agility Digit 5 tanıtıldı: İnsanları algıladığında çömelecek | DonanımHaberURL: https://www.donanimhaber.com/agility-digit-5-tanitildi-insanlari-algiladiginda-comelecek--210544 Description: Agility Robotics, insanları algıladığında duran, uzaklaşan veya çömelebilen insansı robotu Digit 5'i tanıttı. Robot, 2027'de fabrikalarda ve depolarda çalışmaya başlayacak. Content:
Teknoloji ve bilim dünyasını seven ve takip etmekten büyük zevk alan Metin, öğrendiklerini ise DonanımHaber okuyucuları ile paylaşır. Digit 5, yakındaki insanları algıladığında duruma göre farklı tepkiler verebiliyor. Bir kişiyi uzaktan tespit ettiğinde yolunu değiştirebiliyor veya geçişine izin vermek için durabiliyor. Bir insan, robota yaklaştığında ise çömelerek oturma pozisyonuna geçebiliyor. Agility Robotics, insansı robotların ticari kullanımında güvenliği önemli bir konu olarak görüyor. Şirket, 2024 yılında Atlanta'daki GXO deposunda Digit robotlarını tam zamanlı ticari operasyonlarda kullanmaya başlamıştı. Bu dron neredeyse görünmez olabiliyor 1 hf. önce eklendi Önceki Digit modelleri, Kuzey Amerika'daki fabrika ve depolarda 65.000 saatin üzerinde çalışma gerçekleştirdi. GXO, Schaeffler, Amazon ve Toyota Motor Manufacturing Canada, robotları kullanan şirketler arasında yer alıyor. Daha fazla yük taşıyabilecek Tam Boyutta Gör Digit 5, çevresindeki insanları algılamak için yapay zeka algoritmaları ve farklı görüntü tabanlı sensörler kullanıyor. Robotun işlem altyapısında Nvidia Thor IGX donanımı bulunuyor. Sistem ayrıca robotik güvenlik uygulamalarına yönelik Nvidia Halos for Robotics yazılımıyla birlikte çalışıyor. Digit 5'in geliştirilmiş bacak tasarımı, robotun yaklaşık 22,7 kilogramlık yükleri tekrarlı şekilde kaldırmasına imkan tanıyor. Robot, tek şarjla 90 dakika çalışabiliyor ve yalnızca dokuz dakikada yeniden şarj edilebiliyor. Agility, bu sayede Digit 5'in 24 saatlik süreçte 20 saati aşan çalışma gerçekleştirebileceğini belirtiyor. Digit 5'in boyu ise yaklaşık 180 santimetreye ulaşıyor. Robotun erişim yüksekliği 2,19 metre seviyesinde. Digit 4'te ise bu değer yaklaşık 1,68 metreydi. Yeni model, bu sayede yetişkinlerin ulaşabildiği raflara erişebiliyor. Değiştirilebilir tutucu tasarımı sayesinde robota farklı eller ve uç efektörleri takılabiliyor. Yapay zeka tabanlı yeni becerilerle birlikte robotun taşıma, paketleme ve ürün yerleştirme gibi daha fazla görevi yerine getirmesi amaçlanıyor. Kaynakça https://www.agilityrobotics.com/content/agility-unveils-digit-5-humanoid-robot-built-for-cooperatively-safe-work-at-scale https://arstechnica.com/ai/2026/09/agilitys-new-humanoid-robot-will-stop-squat-to-avoid-harming-human-coworkers/ Yorum Yaz Paylaş Tweetle Eposta ile Paylaşın başlıklı bu arkadaşınıza postalayın. Anasayfa Popüler Bilim Zamazingo Haberleri Agility Digit 5 tanıtıldı: İnsanları algıladığında çömelecek Bu haberi ve diğer DH içeriklerini, gelişmiş mobil uygulamamızı kullanarak görüntüleyin: Daha Fazla Video Sessiz Video Tercihleri Otomatik yükle ve oynat Video bitince sonrakine geç donanimhabercom Instagram Takip Et Güneş enerjisiyle şarj olabilen elektrikli traktör K kafkas kartalı ks 41 dakika önce YA RAKADAŞIM NE REKLAMI YA BEZDİ İNSANLAR ARTIK HER PLATFORMDA REKLAM GÖRMEKTEN. Önceki Digit modelleri, Kuzey Amerika'daki fabrika ve depolarda 65.000 saatin üzerinde çalışma gerçekleştirdi. GXO, Schaeffler, Amazon ve Toyota Motor Manufacturing Canada, robotları kullanan şirketler arasında yer alıyor. Digit 5'in geliştirilmiş bacak tasarımı, robotun yaklaşık 22,7 kilogramlık yükleri tekrarlı şekilde kaldırmasına imkan tanıyor. Robot, tek şarjla 90 dakika çalışabiliyor ve yalnızca dokuz dakikada yeniden şarj edilebiliyor. Agility, bu sayede Digit 5'in 24 saatlik süreçte 20 saati aşan çalışma gerçekleştirebileceğini belirtiyor. Digit 5'in boyu ise yaklaşık 180 santimetreye ulaşıyor. Robotun erişim yüksekliği 2,19 metre seviyesinde. Digit 4'te ise bu değer yaklaşık 1,68 metreydi. Yeni model, bu sayede yetişkinlerin ulaşabildiği raflara erişebiliyor. Değiştirilebilir tutucu tasarımı sayesinde robota farklı eller ve uç efektörleri takılabiliyor. Yapay zeka tabanlı yeni becerilerle birlikte robotun taşıma, paketleme ve ürün yerleştirme gibi daha fazla görevi yerine getirmesi amaçlanıyor. Güneş enerjisiyle şarj olabilen elektrikli traktör YA RAKADAŞIM NE REKLAMI YA BEZDİ İNSANLAR ARTIK HER PLATFORMDA REKLAM GÖRMEKTEN. {{Description}} https://www.amazon.com.tr/dp/B0DGGT449L https://www.amazon.com.tr/dp/B0GHPYD2KK https://www.n11.com/urun/dji-neo-2-fly-more-combo-rc-n3-kumandali-drone-dji-turkiye-garantili-116633969?magaza=mediamarkt https://www.amazon.com.tr/dp/B0BRSK9K5K https://kamusondakika.net/bankalar/akbank-emekli-promosyonuna-2-kat-zam-yapti-7175h https://www.migros.com.tr/namet-soslu-dana-parmak-kofte-150-100-g-p-185199f
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| Agility Robotics представила робота Digit 5 — он замирает или … | https://3dnews.ru/1148546/agility-robot… | 10 | Sep 21, 2026 16:00 | active | |
Agility Robotics представила робота Digit 5 — он замирает или приседает при виде человека, но так и задуманоDescription: Agility Robotics представила первого человекоподобного робота Digit 5, спроектированного для безопасной работы рядом с человеком. Это открывает широкие возможности для его применения на складах и автозаводах, устраняя необходимость организовывать изолированные рабочие зоны и устанавливать физические ограждения.. Content:
Agility Robotics представила первого человекоподобного робота Digit 5, спроектированного для безопасной работы рядом с человеком. Это открывает широкие возможности для его применения на складах и автозаводах, устраняя необходимость организовывать изолированные рабочие зоны и устанавливать физические ограждения. Источник изображений: Agility Robotics Обнаружив человека на расстоянии, Agility Digit 5 самостоятельно принимает меры предосторожности: смещается в сторону, чтобы избежать столкновения; замирает на месте, чтобы дать человеку пройти; или даже приседает, если человек оказывается в непосредственной близости. Первые клиенты получат Digit 5 в первой половине 2027 года, а в широкую продажу робот поступит к концу того же года. Сейчас Agility переоборудует свой завод в орегонском Сейлеме, чтобы наладить производство новой модели человекоподобного робота. Безопасность остаётся ключевым фактором, сдерживающим промышленное применение человекоподобных роботов, считают в компании; при этом роботы Agility стали первыми из вышедших в режим постоянной коммерческой эксплуатации: в 2024 году они начали работать на складе GXO в Атланте. К настоящему моменту они наработали более 65 000 часов на складах и заводах по всей Северной Америке. Провели испытания или внедрили роботов Digit такие компании как GXO, Schaeffler, Amazon и Toyota Motor Manufacturing Canada. Сейчас американские и китайские компании соревнуются в наращивании объёмов производства и выводе роботов на рабочие площадки, но вопрос безопасности остаётся «камнем преткновения для всех». Модель Agility Digit 5 обещает более «детализированный» подход к этому вопросу при взаимодействии человека с роботами — простым отключением с последующим включением проблему не решить. Система управления парком роботов Agility может взаимодействовать с внешними системами безопасности на предприятиях заказчиков. Для обнаружения людей поблизости Digit 5 использует целый комплекс бортовых датчиков и алгоритмы искусственного интеллекта. В качестве аппаратной платформы выступает Nvidia Thor IGX, функции безопасности реализуются на основе программного стека Nvidia Halos for Robotics. При разработке функций безопасности Agility участвовала в рабочей группе Международной организации по стандартизации (ISO) по созданию международного стандарта безопасности для промышленных мобильных роботов — ISO 25785-1. Сейчас он находится на стадии рассмотрения комитетом, после чего его вынесут на голосование. Помимо способности безопасно работать рядом с человеком, Digit 5 может похвастаться усовершенствованной конструкцией ног, позволяющей роботу выполнять все работы по поднятию тяжестей — это могут быть грузы массой до 22,7 кг. Рост робота составляет 180,3 см, он способен дотягиваться до предметов на высоте до 219,5 см. Ресурса аккумулятора хватает на 90 минут работы, полная зарядка батареи занимает всего 9 минут — Digit 5 может работать более 20 часов в сутки. Конструкция робота предусматривает возможность быстрой замены кистей рук для выполнения различных задач по манипулированию объектами — он больше не ограничивается простым перемещением контейнеров с грузом. Digit 5 умеет укладывать товары на палеты и приостанавливать работы на время их замены сотрудниками, а также безопасно перемещаться по проходам, где могут находиться люди. Источник: © 1997—2026 3DNews При цитировании документа ссылка на сайт с указанием автора обязательна. Полное заимствование документа является нарушениемроссийского и международного законодательства и возможно только с согласия редакции 3DNews. Сайт 3DNews использует файлы cookie и сервисы аналитики. Это помогает нам улучшать контент и работу Cайта. Во время посещения Cайта вы соглашаетесь с Пользовательским соглашением и даёте согласие на обработку и передачу (в т.ч. трансграничную) персональных данных, использование Cайтом файлов cookie и метрических программ.
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| Digit 5 di Agility Robotics si accovaccia per evitare gli … | https://www.tecnoandroid.it/news/digit-… | 10 | Sep 21, 2026 16:00 | active | |
Digit 5 di Agility Robotics si accovaccia per evitare gli urti - TecnoAndroidDescription: Il robot umanoide Digit 5 si accovaccia quando una persona si avvicina, usa chip Nvidia Thor IGX e attende lo standard ISO 25785-1. Content:
Accovacciarsi per non far male a nessuno: è questa la mossa che distingue Digit 5, il nuovo robot umanoide di Agility Robotics pensato fin dal progetto per lavorare accanto alle persone senza bisogno di gabbie, barriere o celle isolate. Quando individua qualcuno a distanza, si sposta oppure resta fermo per lasciar passare. Se invece la persona si avvicina davvero tanto, il robot può scegliere di piegarsi sulle gambe e assumere una posizione seduta, riducendo al minimo il rischio di urti. Non è un dettaglio da poco, perché la sicurezza è sempre stata il vero collo di bottiglia per portare gli umanoidi dentro magazzini e fabbriche automobilistiche. “Il robot è stato progettato con un sistema di movimento sicuro piuttosto complesso, capace di adottare diverse contromisure a seconda del tipo esatto di presenza umana rilevata”, ha spiegato Pras Velagapudi, chief technology officer di Agility. L’idea è superare la logica dell’interruttore, quella del robot che si spegne di colpo e poi va riavviato, per passare a un approccio molto più granulare. TecnoAndroid · su Google Seguici su Google e non perdere nulla Aggiungi TecnoAndroid alle tue fonti preferite su Discover e segui il nostro profilo Google: le notizie tech più importanti arrivano direttamente sul tuo telefono. Per riconoscere chi gli sta intorno, Digit 5 combina algoritmi di intelligenza artificiale e una serie di sensori multimodali a bordo. Velagapudi non ha voluto dire esattamente quali, limitandosi a descriverli come “alcuni tipi diversi di sensori basati sulla visione”. Il cervello di calcolo è l’hardware Nvidia Thor IGX, pensato per robot e dispositivi medicali, affiancato dall’integrazione del sistema Nvidia Halos for Robotics, che fornisce lo stack software per le applicazioni di sicurezza robotica. “Digit è equipaggiato con questo Thor IGX e siamo tra i primi ad adottarlo per arrivare a un’applicazione funzionalmente sicura basata su quella tecnologia”, ha aggiunto Velagapudi. “È davvero il cuore del calcolo del nostro prodotto ed è parte del dossier di sicurezza che stiamo costruendo.” In parallelo, Agility ha partecipato a un gruppo di lavoro per definire uno standard internazionale sulla sicurezza dei robot mobili industriali attraverso l’ISO. Lo standard, identificato dalla sigla ISO 25785-1, è in fase di revisione da parte del comitato e dovrà poi passare al voto degli 89 Paesi membri con diritto di voto. C’è anche un sistema di gestione della flotta robotica capace di dialogare con i sistemi di sicurezza già presenti negli stabilimenti dei clienti, così da non costringere nessuno a rifare tutto da zero. Oltre alla sicurezza, il salto riguarda i muscoli. Le gambe sono state ridisegnate e permettono al robot umanoide di sollevare ripetutamente carichi fino a 50 libbre, circa 23 chili. Un valore che copre tutte le attività di sollevamento da parte di una singola persona secondo le regole dell’agenzia statunitense per la sicurezza sul lavoro OSHA. La batteria garantisce 90 minuti di autonomia e si ricarica in appena nove minuti, il che si traduce in oltre 20 ore di lavoro effettivo nell’arco di una giornata. Rispetto al predecessore Digit 4, il nuovo modello è più alto, 5 piedi e 11 pollici, poco meno di 1,80 metri, e arriva a toccare 7,2 piedi in altezza contro i 5,5 piedi della versione precedente. Tradotto: raggiunge gli stessi scaffali di un adulto di corporatura media. I gripper sono intercambiabili, quindi si possono montare rapidamente mani diverse a seconda del compito, e insieme alle nuove abilità basate su AI questo permette al robot di fare molto più che spostare cassette. Può impilare e confezionare merce sui pallet e fermarsi mentre un operatore cambia il pallet, oppure muoversi lungo corsie dove ci sono altre persone al lavoro. “In quegli scenari non è davvero pratico rinchiudere il robot dentro una gabbia di sicurezza fisica”, ha osservato Velagapudi. L’accesso anticipato per i clienti è previsto entro la prima metà del 2027, con disponibilità generale entro la fine dello stesso anno. Agility sta riattrezzando lo stabilimento RoboFab di Salem, in Oregon, per produrlo. L’azienda, sempre con sede a Salem, nel 2024 è stata la prima a impiegare umanoidi in operazioni commerciali a tempo pieno in un magazzino GXO ad Atlanta, e da allora le versioni precedenti di Digit hanno accumulato oltre 65.000 ore di lavoro tra magazzini e fabbriche del Nord America, con clienti come GXO, Schaeffler, Amazon e Toyota Motor Manufacturing Canada. Fonte: TecnoAndroid Notizie, recensioni e approfondimenti di tecnologia, scienza e innovazione. Non solo Android. Ultimo aggiornamento: 2026-09-21 17:59:00 2012 – 2026 © Tecnoandroid.it – Gestito dalla STARGATE SRLS – P.Iva: 15525681001 Testata telematica quotidiana registrata al Tribunale di Roma CON DECRETO N° 225/2015, editore STARGATE SRLS. Tutti i marchi riportati appartengono ai legittimi proprietari. Questo articolo potrebbe includere collegamenti affiliati: eventuali acquisti o ordini realizzati attraverso questi link contribuiranno a fornire una commissione al nostro sito. 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| Agility Robotics Unveils Digit 5 Humanoid Robot Shipping in 2026 | https://www.webpronews.com/agility-robo… | 7 | Sep 21, 2026 16:00 | active | |
Agility Robotics Unveils Digit 5 Humanoid Robot Shipping in 2026URL: https://www.webpronews.com/agility-robotics-unveils-digit-5-humanoid-robot-shipping-in-2026/ Description: Agility Robotics unveiled an early preview of Digit 5, its refined next-generation humanoid robot set to ship to select customers in 2026. The 5'9", 160-pound model features improved hands with 16 degrees of freedom, a sleeker sensor head, faster walking speeds, enhanced battery life, and advanced AI for autonomous task handling in logistics and manufacturing. Content:
Agility Robotics has provided an early preview of its next-generation humanoid robot, Digit 5, which the company plans to begin shipping to select customers in 2026. The announcement, covered in detail by Business Insider, shows a machine that looks noticeably more refined than its predecessors while retaining the core bipedal design that has become the Oregon-based company’s signature. Digit 5 stands roughly 5 feet 9 inches tall and weighs about 160 pounds, dimensions that allow it to operate comfortably in spaces built for adult humans. The robot features a completely redesigned torso that houses improved actuators and a more powerful battery system capable of delivering up to two hours of continuous operation under moderate loads. Engineers at Agility have focused particular attention on the hands, which now include 16 degrees of freedom per hand and enhanced tactile sensing that lets the machine detect both the force and texture of objects it grasps. One of the most visible changes appears in the robot’s head. Rather than the somewhat cartoonish face of earlier versions, Digit 5 carries a sleek sensor array protected by a smooth polycarbonate cover. The head contains multiple depth cameras, an improved IMU, and a high-resolution RGB camera that feeds into an updated vision system. This setup allows the robot to recognize objects more quickly and to build more accurate maps of its surroundings in real time. The company has also modified the legs and feet. Digit 5 uses a new ankle design that increases range of motion while reducing the likelihood of ankle roll during uneven terrain navigation. The feet themselves incorporate better force sensors and a slightly wider base that improves stability when the robot carries heavy objects or moves at faster walking speeds. According to Agility’s engineering team, these changes allow Digit 5 to walk at sustained speeds of up to 1.8 meters per second, roughly double the comfortable pace of the previous model. Much of the performance gain comes from advances in the control software. Agility has spent the past two years training new neural networks using both simulation and real-world data collected from its existing fleet of Digit robots. The result is a system that can recover from unexpected disturbances more gracefully and can plan multi-step manipulation sequences without constant human supervision. For example, the robot can now pick up a cardboard box, carry it across a warehouse floor while avoiding moving forklifts, and then place the box on a shelf at a precise height without needing step-by-step instructions for each segment of the task. Agility Robotics intends to position Digit 5 primarily for logistics and light manufacturing work. The company has already signed agreements with several large distribution centers to begin pilot programs once the new model becomes available. In these environments, the robot will handle repetitive tasks such as transferring totes between conveyor systems, sorting packages by destination, and performing basic quality checks on assembled products. Because Digit 5 can use the same tools and workstations designed for human workers, companies can introduce the robots gradually without redesigning their entire facilities. The decision to keep the robot’s form factor compatible with existing infrastructure reflects lessons learned from earlier deployments. Previous versions of Digit sometimes struggled in tight spaces or when required to interact with equipment built strictly for human hands. Digit 5 addresses many of those pain points through smaller wrist diameters, improved finger strength, and better wrist rotation that allows it to turn door knobs, operate hand tools, and manipulate clothing or soft materials with greater reliability. Battery technology represents another area of significant progress. The new lithium-ion pack stores more energy in a smaller volume and can be hot-swapped in less than 90 seconds. This feature matters for industrial users who need machines to operate for full shifts without long charging interruptions. Agility has also incorporated wireless charging pads into some of its demonstration stations, allowing the robot to top off its battery while standing idle between tasks. Safety remains a central concern for any company deploying legged robots around people. Digit 5 includes multiple layers of protection, including force-limiting joints that yield when they encounter unexpected resistance, emergency stop circuits that can be triggered by nearby workers, and a new compliance controller that makes the machine feel softer during physical contact. The robot can also detect when a human enters its immediate workspace and automatically slow its movements or choose alternative paths to avoid collisions. Agility has not yet disclosed exact pricing for Digit 5, though executives have indicated that the robot will cost significantly less per unit than earlier generations once production scales. The company currently operates a factory in Salem, Oregon, that can produce roughly 10,000 robots per year at full capacity. Plans call for expanding that facility and potentially opening a second manufacturing site as demand grows. The broader context for this announcement involves increasing competition in the humanoid robot sector. Several other companies have shown prototypes with similar capabilities, and major technology firms have begun investing heavily in the space. Agility maintains that its focus on practical, near-term applications in logistics gives it an advantage over groups pursuing more general-purpose machines that may take longer to reach commercial readiness. Early feedback from companies that have tested previous Digit models has been mixed but generally positive regarding the robot’s ability to perform specific warehouse tasks. Workers report that the machines reduce physical strain on human employees by handling the most repetitive lifting jobs. However, some facilities have noted that the robots still require occasional human intervention when faced with unexpected situations such as damaged packaging or misplaced items. Agility’s development team has used that feedback to guide improvements in Digit 5. The new model includes a more sophisticated reasoning engine that can evaluate multiple possible solutions when it encounters a problem and select the one most likely to succeed. The system can also ask for human assistance in a natural way by displaying simple questions on a chest-mounted screen or speaking through an integrated speaker. Training the robot has become more efficient as well. Rather than programming each new task from scratch, Agility now uses demonstration learning techniques that allow a human operator to show the robot how to perform an action a few times before the machine can replicate it independently. This approach reduces the time needed to onboard the robot for new jobs from weeks to days in many cases. Looking further ahead, Agility envisions Digit 5 serving as a platform for additional capabilities. The modular design allows customers to add specialized tools or sensors for particular industries. A version equipped with chemical sensors could inspect food processing lines, while another fitted with high-resolution thermal cameras might monitor electrical equipment in data centers. The company has also begun exploring healthcare applications, although those uses will require additional regulatory approvals and safety certifications. The software stack that runs Digit 5 deserves separate attention. Built on a combination of classical control algorithms and modern machine learning models, the system maintains a digital twin of the robot and its environment that updates dozens of times per second. This digital twin helps the robot predict the consequences of its actions before it commits to them, reducing mistakes and improving overall reliability. Updates to the software can be pushed over the air, meaning robots in the field can gain new skills without physical modification. Power efficiency has improved to the point where Digit 5 can perform light manipulation tasks while consuming roughly the same electricity as a large household appliance. This efficiency matters both for operating costs and for the environmental impact of large robot deployments. Agility has worked with battery manufacturers to ensure that the materials used in the packs come from sources with strong labor and environmental standards. The company has also paid close attention to how the robot looks and moves. Earlier versions sometimes appeared jerky or unstable to human observers, which could create discomfort in shared workspaces. Digit 5 exhibits smoother gait patterns and more predictable motion profiles that make it easier for people to work alongside the machine. The robot can even make small head movements that signal its attention and intentions, a subtle form of nonverbal communication that helps build trust with human colleagues. Production timelines call for the first customer units to leave the factory in early 2026, with initial shipments going to partners who have already participated in pilot programs. Agility plans to ramp up manufacturing steadily throughout the year, aiming to have several hundred robots in commercial service by the end of 2026. The company will continue selling and supporting earlier Digit models while gradually transitioning customers to the newer platform. Financial backers have expressed confidence in the roadmap. Agility completed a substantial funding round in 2024 that valued the company at more than $1 billion and provided resources to expand both engineering and manufacturing teams. That investment reflects growing belief among venture capitalists and strategic partners that humanoid robots will find practical roles in the labor market within the next few years rather than decades. Challenges remain. The cost of building and maintaining these machines still exceeds many potential customers’ budgets for automation. Technical issues around long-duration autonomy, dexterous manipulation of soft or delicate objects, and reliable operation in highly unstructured environments continue to require attention. Regulatory frameworks for robots that share physical spaces with people are still developing, creating uncertainty for companies planning large deployments. Despite these hurdles, the progress shown in Digit 5 suggests that practical humanoid robots are moving from science fiction toward everyday tools. The machine does not claim to replace human workers entirely, but rather to take on specific physically demanding or repetitive tasks that contribute to labor shortages in logistics, manufacturing, and related fields. As the technology matures and prices fall, the range of viable applications will likely expand. Agility Robotics has indicated that it will share additional technical details and demonstration videos in the coming months. The company plans to host several in-person events where potential customers can interact directly with Digit 5 prototypes and evaluate the robot’s suitability for their particular operations. These interactions will help refine final specifications before full production begins. The preview of Digit 5 marks a concrete step in the commercialization of advanced robotics. By focusing on reliability, safety, and compatibility with existing workplaces, Agility has positioned its latest creation as a machine that companies can realistically consider adding to their workforces in the near future. Whether the robot ultimately meets the high expectations set by its developers will become clearer as the first units begin real-world operations in 2026. For now, the early look offers a promising view of how physical automation may develop over the next decade. 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| Agility Robotics представила робота Digit 5 с приоритетом на безопасность … | https://habr.com/ru/news/1082868/ | 7 | Sep 21, 2026 16:00 | active | |
Agility Robotics представила робота Digit 5 с приоритетом на безопасность людей рядом / ХабрURL: https://habr.com/ru/news/1082868/ Description: Американская Agility Robotics представила первого человекоподобного робота, который создан с учётом безопасного функционирования рядом с людьми. Новинка под названием Digit... Content:
Американская Agility Robotics представила первого человекоподобного робота, который создан с учётом безопасного функционирования рядом с людьми. Новинка под названием Digit 5 открывает возможности применения роботов на складах и заводах по сборке автомобилей без необходимости использования изолированных рабочих зон и физических разделительных барьеров. Когда Digit 5 обнаруживает человека на расстоянии, он может автономно принять меры предосторожности. Например, робот способен остановиться или переместиться, чтобы избежать столкновения с человеком. Если люди находятся рядом с роботом, он может присесть и принять сидячее положение. Клиенты смогут получить ранний доступ к Digit 5 в первой половине 2027 года, а к концу того же года робот станет доступен для всех желающих. Для производства новой модели Agility переоборудовала свой завод RoboFab в Салеме, штат Орегон. Руководство компании постоянно говорило о безопасности как о ключевом ограничивающем факторе при внедрении человекоподобных роботов на предприятиях. В 2024 году Agility первой ввела человекоподобных роботов в постоянную коммерческую эксплуатацию на складе GXO в Атланте, штат Джорджия. С тех пор ранние версии робота Digit отработали более 65 000 часов на складах и заводах по всей Северной Америке. В тестировании и внедрении роботов компании участвовали GXO, Schaeffler, Amazon и Toyota Motor Manufacturing Canada. В последние годы конкуренция за внедрение гуманоидных роботов только усилилась. Американские и китайские компании стремятся нарастить производство и использовать роботов на всё большем числе рабочих месте. Однако проблема безопасности стала препятствием для всех, заявил соучредитель Agility и исследователь робототехники Университета штата Орегон Джонатан Хёрст. Технический директор компании Agility Прас Велагапуди рассказал, что Digit 5 обеспечит более детальный подход к обеспечению безопасности людей вблизи роботов, в отличие от простого полного выключения и повторного включения машины. Он отметил, что методы управления парком роботов Agility способны взаимодействовать с внешними системами безопасности на рабочем месте клиента. Digit 5 использует алгоритмы искусственного интеллекта вместе со встроенным набором мультимодальных датчиков для обнаружения людей поблизости. Велагапуди отказался раскрыть, какие именно типы датчиков использует робот для отслеживания людей. По его словам, этот набор состоит из «нескольких различных типов датчиков, основанных на машинном зрении». Новинка Agility основана на аппаратной платформе Nvidia Thor IGX, обеспечивающей вычисления ИИ для роботов и медицинских устройств. Также платформа интегрирует программный стек для приложений обеспечения безопасности робототехники Nvidia Halos for Robotics. Agility разработала функции безопасности Digit 5. Также компания участвовала в рабочей группе по созданию международного стандарта безопасности для промышленных мобильных роботов Международной организации по стандартизации. На рассмотрении комитета находится стандарт ISO 25785–1, за который будут голосовать 89 стран‑членов международной организации. Также Digit 5 получил улучшенную конструкцию ног по сравнению со своим предшественником, что позволяет ему многократно поднимать грузы весом до 22 кг. Благодаря этой возможности робот может выполнять задачи по подъёму грузов, рассчитанных на одного человека, как это определено правилами техники безопасности Управления по охране труда США. Человекоподобный робот оснащён батареей, обеспечивающей до 90 минут автономной работы и заряжающейся всего за 9 минут. Высота робота составляет 180 см — он выше Digit 4. Максимальная высота, на которую может дотянуться Digit 5, достигает 220 см. Робот оснащён сменными захватами, позволяющими быстро устанавливать новые манипуляторы для выполнения различных задач. Информационная служба Хабра Программист Microsoft Марк Руссинович, который в настоящее время является техническим директором Microsoft Azure, использовал искусственный интеллект, чтобы перенести инструмент Windows ZoomIt, который он поддерживает более 20 лет, на macOS за выходные. Ваш аккаунт Разделы Информация Услуги
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| Digit 5 : Agility Robotics dévoile un robot humanoïde capable … | https://kulturegeek.fr/news-359529/digi… | 10 | Sep 21, 2026 16:00 | active | |
Digit 5 : Agility Robotics dévoile un robot humanoïde capable de "faire attention" aux travailleurs humains qui l'entourent - KultureGeekDescription: Pour faire entrer les robots humanoïdes dans les entreprises, la puissance et l'autonomie ne suffisent plus. Agility Robotics vient de présenter Digit 5, Content:
Pour faire entrer les robots humanoïdes dans les entreprises, la puissance et l’autonomie ne suffisent plus. Agility Robotics vient de présenter Digit 5, une nouvelle génération de son robot industriel dont la principale innovation concerne la sécurité. L’objectif est de permettre aux machines d’évoluer aux côtés des salariés sans imposer systématiquement les barrières physiques qui entourent les automates traditionnels. Digit 5 dispose de plusieurs technologies de détection associées à des algorithmes d’intelligence artificielle. Lorsqu’une personne s’approche, le robot peut modifier sa trajectoire, interrompre ses mouvements ou adopter une position accroupie pour limiter les risques d’accident. Cette réponse graduée constitue une différence importante face aux systèmes industriels qui reposent principalement sur l’isolement des machines. La sécurité devient ainsi une fonction intégrée au comportement du robot, même si son efficacité devra être démontrée dans les environnements de production. Cette problématique mobilise déjà d’autres constructeurs, notamment HIGEN RNM avec ses actionneurs capables de réagir rapidement aux perturbations. Le nouvel humanoïde peut soulever des charges atteignant 23 kg et accéder à des étagères situées jusqu’à 2,20 mètres de hauteur. Son autonomie annoncée atteint 90 minutes, avec une recharge adaptée aux cycles de travail industriels. Agility revendique également plus de 65 000 heures d’exploitation cumulées sur ses précédents déploiements commerciaux. Son robot Digit a notamment été expérimenté dans les entrepôts d’Amazon. Pour Peggy Johnson, directrice générale d’Agility Robotics, Digit 5 permet de « lever un obstacle majeur au déploiement à grande échelle des robots humanoïdes dans les environnements industriels ». Alors que Renault expérimente également des humanoïdes sur ses lignes de production, Digit 5 illustre une évolution du secteur : l’enjeu n’est plus seulement d’automatiser les tâches physiques, mais de rendre cette automatisation compatible avec la présence quotidienne des travailleurs. Signaler une erreur dans le texte Merci de nous avoir signalé l'erreur, nous allons corriger cela rapidement. Δ Nous nous réservons le droit de supprimer les commentaires qui ne respectent pas ces règles Chaque jour nous dénichons pour vous des promos sur les produits High-Tech pour vous faire économiser le plus d’argent possible. Voici... Le projet d’accord à 400 millions de dollars entre TikTok, ByteDance et le ministère américain de la Justice rencontre un... La Chine ralentit les projets d’introduction en Bourse de plusieurs fabricants de robots humanoïdes, alors que les autorités... Des chercheurs de l’Institut indien de technologie d’Hyderabad ont fait décoller un microdrone depuis un hexarotor déjà... Le retour de Resident Evil au cinéma dépasse largement les attentes de Sony Pictures. Sorti le 18 septembre, le reboot réalisé... Photo et vidéo Photo et vidéo Style de vie Jeux Photo et vidéo Photo et vidéo Utilitaires Jeux Jeux Jeux Drame Indépendant Comédie Action et aventure Comédie Enfants / famille Enfants / famille Horreur 21 Sep. 2026 • 17:37 21 Sep. 2026 • 17:13 21 Sep. 2026 • 16:36 21 Sep. 2026 • 15:06 Actualité High-Tech, Culture Geek et comparateur de prix Recherchez le meilleur prix des produits Hi-tech Recherchez des articles sur le site
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| Digit 5: Agility Robotics lässt Humanoiden ohne Zaun ins Lager | https://www.business-punk.com/digit-5-a… | 6 | Sep 21, 2026 16:00 | active | |
Digit 5: Agility Robotics lässt Humanoiden ohne Zaun ins LagerDescription: Agility Robotics zeigt Digit 5: humanoider Roboter ohne Schutzzaun, 300 Mio. Dollar Orders, EU-Start 2027. Content:
Agility Robotics stellt mit Digit 5 einen Humanoiden vor, der ohne Schutzzaun neben Menschen arbeiten soll. 300 Mio. Dollar Orders zeigen: Der Markt glaubt daran, bevor der Sicherheitsbeweis im Alltag steht. Agility Robotics hat Digit 5 vorgestellt, den Nachfolger seines humanoiden Lagerroboters. Der zentrale Unterschied zum Vorgänger: Digit 5 soll ohne die bislang üblichen Trennwände direkt neben Menschen arbeiten dürfen, wie heise berichtet. Möglich machen soll das eine Kombination aus KI-gestützter Personenerkennung, Sensorik und Nvidias Sicherheitsplattform Halos, für die Agility laut The Decoder erster Partner ist. Der Roboter soll anhalten, ausweichen oder sich hinsetzen, wenn Menschen zu nah kommen und vorab optische sowie akustische Signale senden. Bislang musste selbst Digit 4 während der Arbeit durch Trennwände von Kolleginnen und Kollegen separiert werden, weil umfallende Humanoide reale Verletzungsrisiken bergen, so heise. Dass Digit 5 diese Barriere aufheben soll, ist deshalb mehr als ein technisches Detail, es verändert die Betriebsorganisation ganzer Lagerhallen. Agility stützt sein Sicherheitsversprechen auf mehr als 65.000 Betriebsstunden des Vorgängers bei Kunden wie GXO, Amazon, Schaeffler und Toyota sowie auf eine laut Unternehmen bestandene OSHA-Sicherheitsprüfung von Digit 4 auf einer Produktionslinie. Bei GXO habe der Vorgänger laut The Decoder rund 100.000 Behälter mit etwa 98 Prozent Genauigkeit bewegt. Ein unabhängiger Nachweis für Digit 5 selbst steht damit noch aus, die Erfahrungswerte stammen vom Vorgängermodell und aus drei Jahren Industrieeinsatz. Technisch legt Digit 5 spürbar zu: Der Roboter hebt laut The Decoder bis zu 22,7 Kilogramm, 40 Prozent mehr als Digit 4 und wächst auf 1,81 Meter sowie 129 Kilogramm Gewicht, wodurch er laut heise Regale bis 2,2 Meter Höhe erreicht. Zum Vergleich: Digit 4 kam auf 1,75 Meter, 64 Kilogramm Eigengewicht und griff nur bis 1,68 Meter Höhe. Der Akku lädt in neun Minuten für 90 Minuten Laufzeit, ein Verhältnis von 10 zu 1 gegenüber 2 zu 1 beim Vorgänger, was nach Unternehmensangaben mehr als 20 Stunden Arbeit pro Tag im Schichtbetrieb ermöglichen soll. Wechselbare Greifer erlauben laut The Decoder den Wechsel zwischen Behältertransport und Maschinenbestückung. Gefertigt wird in Salem, Oregon, in einer Fabrik mit Kapazität für bis zu 10.000 Roboter pro Jahr, gebaut, entworfen und montiert in den USA, wie Agility betont. Das Briefing für Leute, die die Zukunft bauen — mehrmals die Woche in deinem Postfach Martin Wald analysiert die Schnittstelle zwischen Geld, Macht und Alltag. Seine Themen reichen von Rente, Sozialstaat und Gesundheit bis zu Wirtschaft, Märkten und technologischen Umbrüchen. Er zerlegt politische Entscheidungen, Urteile und Trends auf ihre ökonomischen Folgen und macht sichtbar, wer gewinnt, wer verliert und wo Risiken liegen. Der Blick ist pragmatisch, pointiert und nutzenorientiert. Keine Wohlfühltexte, sondern Einordnung für Menschen, die verstehen wollen, wie Systeme funktionieren und was das für ihre Entscheidungen bedeutet. Das Briefing — mehrmals die Woche im Postfach Die Medienmarke für die Menschen, die die Zukunft bauen. Business is Culture.
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| Agility Robotics представила робота Digit 5 | KV.by | https://www.kv.by/news/1072432-agility-… | 10 | Sep 21, 2026 16:00 | active | |
Agility Robotics представила робота Digit 5 | KV.byURL: https://www.kv.by/news/1072432-agility-robotics-predstavila-robota-digit-5 Description: Agility Robotics представила Digit 5 — первого в своей линейке человекоподобного робота, спроектированного для безопасного сосуществования с людьми. Это открывает широкие возможности применения на складах и в автопроизводстве, исключая необходимость изолированных зон и ограждений. Content:
Agility Robotics представила Digit 5 — первого в своей линейке человекоподобного робота, спроектированного для безопасного сосуществования с людьми. Это открывает широкие возможности применения на складах и в автопроизводстве, исключая необходимость изолированных зон и ограждений. При обнаружении приближающегося человека Digit 5 самостоятельно принимает меры предосторожности: отклоняется в сторону, чтобы избежать столкновения; останавливается, чтобы пропустить человека; при близком расстоянии может даже присесть. Первые клиенты получат Digit 5 в первой половине 2027 года, а массовое внедрение запланировано на конец того же года. Сейчас Agility переоборудует свой завод в Сейлеме, Орегон, под выпуск новой модели. Безопасность остаётся ключевым фактором для промышленного применения гуманоидов, считают в компании; тем не менее роботы Agility стали одними из первых, вышедших на рынок в режиме постоянной коммерческой эксплуатации: в 2024 году они начали работать на складе GXO в Атланте. К настоящему времени роботами Digit оборудованы склады и заводы по Северной Америке на их счет более 65 000 часов работы. Среди пользователей — GXO, Schaeffler, Amazon и Toyota Motor Manufacturing Canada. Сейчас американские и китайские компании соревнуются за рост производственных мощностей и выведение роботов на рабочие площадки, однако вопрос безопасности остаётся «главным препятствием» для всех. Модель Digit 5 обещает более детальный подход к взаимодействию человека и робота — простым отключением проблему не решить. Система управления парком роботов Agility может интегрироваться с внешними системами безопасности на предприятиях заказчиков. Для обнаружения людей Digit 5 использует целый комплекс бортовых датчиков и алгоритмов искусственного интеллекта. В качестве аппаратной базы выступает Nvidia Thor IGX, функции безопасности реализованы на основе программного стека Nvidia Halos for Robotics. При разработке функций безопасности Agility участвовала в рабочей группе ISO по созданию международного стандарта для промышленных мобильных роботов — ISO 25785-1, который сейчас находится на рассмотрении и затем будет вынесен на голосование. Помимо способности безопасно работать рядом с человеком, Digit 5 обладает улучшенной конструкцией ног, что позволяет поднимать грузы массой до 22,7 кг. Рост робота — 180,3 см, досягаемость — 219,5 см. Аккумулятор обеспечивает около 90 минут автономной работы, полная зарядка занимает 9 минут, и робот может работать более 20 часов в сутки. Конструкция предусматривает быструю замену кистей рук для выполнения разных задач по манипуляциям, что расширяет сферы применения за пределами простой перегрузки контейнеров. Digit 5 способен укладывать товары на палеты, приостанавливать работы во время замены сотрудников и безопасно перемещаться по проходам, где могут находиться люди. Опубликовал: KV_newsroom, 18 сентября, 2026 - 13:35 © Компьютерные вести, 1994-2026 16+
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| Unitree CEO predicts AI breakthrough for humanoid robots | The … | https://www.jpost.com/international/art… | 7 | Sep 20, 2026 16:00 | active | |
Unitree CEO predicts AI breakthrough for humanoid robots | The Jerusalem PostURL: https://www.jpost.com/international/article-906057 Description: Humanoid robots remain far from mass deployment, but Unitree’s CEO says the industry could reach a major AI breakthrough within two to three years. Content:
The CEO of Chinese humanoid robot unicorn Unitree 688836.SS said on Thursday the industry is edging towards a "ChatGPT moment" for robot brains, after the company's blockbuster Shanghai listing crystallized China's ambitions to lead the next frontier of AI-powered machines. Shares in China's best-known humanoid robot maker fell 11% on Thursday, a day after soaring nearly sixfold in their market debut, highlighting intense investor enthusiasm for a fledgling sector that enjoys strong backing from Beijing. "We are marching towards a 'ChatGPT moment' in embodied intelligence," said Wang Xingxing, the Hangzhou-based startup's founder and CEO, at a major robot conference in Beijing. The global AI boom that has reshaped the world economy was sparked in late 2022 by ChatGPT's breakthrough large language model, a watershed moment that drove mass adoption. No comparable inflection point has yet emerged for world models, the physical AI simulation systems designed to help robots understand and navigate real-world environments. Wang said the industry is nearing a breakthrough where robots can be placed in unfamiliar environments and complete most tasks through simple voice or text instructions. "We hope that in the future, we can see a robot be introduced into an unfamiliar household and it can achieve approximately 80% of tasks successfully through voice or text commands," he said. "It is an important tipping point for the robot industry to usher in explosive growth." At the same time, Wang cautioned that a major leap in robot software could arrive within two to three years in an optimistic scenario, or within five to 10 years at the latest. "Relative to the mood around World Robot Conference and Unitree’s spectacular IPO, Wang Xingxing was notably sober about current capabilities," said Georg Stieler, head of automation at robotics consultancy Stieler. Unitree is the world's largest producer of robot dogs and the second-biggest maker of humanoid robots by shipments, according to industry data. It rose to fame with impressively choreographed performances of robots dancing and performing kung-fu, showcased on Chinese television, and is increasingly deploying its machines in real-world industrial settings. Its IPO prospectus shows most customers are universities and research institutions. Wang He, founder of Chinese robotic startup Galbot, expects the sector's "ChatGPT moment" to arrive by 2028, defining it as the point when robots can perform about 70% to 80% of everyday tasks without specialized training. "With continued accumulation of data and further technological breakthroughs, we expect to reach the 'ChatGPT moment' for embodied intelligence by 2028," he said. Unitree's Wang said the company's biggest current investment in terms of capital and manpower is in world models and that it is "lagging behind" in the real-world application of physical AI models. He also said humanoids are not yet capable enough for mass deployment, pointing to limitations in the AI models that power robots' decision-making and interactions as the industry's biggest bottleneck. Even so, Wang expects the sector to enter a decade of rapid autonomous evolution of humanoid robots, as the AI boom accelerates and AI language models learn to improve themselves. China delivered over 40,000 humanoids in the first half of this year alone, and accounts for 97% of global shipments of humanoid robots, a Chinese humanoid industry body said in a report Thursday. Beijing is betting that robots can eventually replace human labor in repetitive, low-value and dangerous settings as it faces a shrinking workforce due to demographic decline. While humanoids are gradually being introduced to factory floors and logistics warehouses, they remain less efficient than human workers in most applications. Humanoid robots have also become a new front in US-China technology rivalry. Last month the US Federal Communications Commission banned future imports of foreign-made humanoid and quadruped robots citing national security concerns, in a major blow to Chinese producers. Several companies at the World Robot Conference in Beijing told Reuters they were looking to expand overseas. Copyright ©2026 Jpost Inc. All rights reserved • •
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| Treble Raises $18M to Expand Audio and Voice Development Platform … | https://www.manilatimes.net/2026/09/17/… | 0 | Sep 20, 2026 08:00 | active | |
Treble Raises $18M to Expand Audio and Voice Development Platform for Physical AIDescription: Treble Raises $18M to Expand Audio and Voice Development Platform for Physical AI Content: |
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| Zoomlion Humanoid Robots Shine at World Games | https://incrediblethings.com/zoomlion-h… | 10 | Sep 18, 2026 16:00 | active | |
Zoomlion Humanoid Robots Shine at World GamesURL: https://incrediblethings.com/zoomlion-humanoid-robots-world-games/ Description: Zoomlion humanoid robots compete in Beijing World Games, proving AI gait, stability & industrial strength. Content:
Share this content with others Your email address will not be published. Required fields are marked * Comment * Name * Email * Save my name, email, and website in this browser for the next time I comment. No Readers' Pick yet. At the recently held World Humanoid Robot Games, Zoomlion’s humanoid robots competed in the standing high jump, 100-meter sprint and standing long jump. They delivered steady results across all three events – while proving their ability to handle high-speed movement and complex control. Their consistent performances also highlighted the tight integration of hardware architecture, AI algorithms and motion-control systems. A press release from Zoomlion disclosed that the same robots are now working daily in Zoomlion’s smart factories, sorting materials, tying cable harnesses, assembling parts and carrying out inspections. And this mix of competition testing and factory use is helping Zoomlion to improve its robots and expand their industrial applications. The World Humanoid Robot Games provided demanding conditions for evaluating gait algorithms, joint responsiveness and overall stability under high-speed motion and complex control conditions, while providing data to further refine the company’s full-stack capabilities across algorithms, software, hardware and industrial applications. Zoomlion’s robots delivered consistent performances across all three events, showcasing the integration of their hardware architecture, AI algorithms and motion-control systems. Their showing reinforced the company’s position among the field’s leading robotics developers. Zeng Guang, general manager of Zoomlion’s ZValley, said the events measured more than speed and strength, providing a broader assessment of the company’s AI capabilities and its ability to deploy robots in real-world settings. Competing alongside teams from around the world also allowed Zoomlion to benchmark its technology and refine its systems and products for greater reliability and adaptability in industrial settings. The company has deployed dozens of its humanoid robots across its smart factories, where they perform material sorting, cable-harness tying, component preassembly, precision assembly, finished-product inspection and factory patrols. The bipedal Z01 uses AI-powered imitation learning to organize randomly placed objects and precisely arrange and tie cable harnesses. The wheeled Z03 can independently handle precision processes, including preassembling mirrors for industrial equipment, to meet the accuracy requirements of production lines. Zoomlion draws on real-world operating data from these deployments, as well as more than 300 intelligent production lines and over 2,000 industrial robots running year-round. This data supports the continued refinement of AI models, algorithms and robot performance, creating a cycle in which technology enables deployment and real-world experience informs further development. Zoomlion’s portfolio includes bipedal and wheeled humanoid robots as well as quadruped robots, backed by in-house capabilities spanning core components, operating systems, complete robot systems and industrial applications. The company is among the few AI-native robotics companies to combine full-stack humanoid robot development, deployment at scale and continuous improvement based on real-world data. It is also the only high-end equipment manufacturer to have developed and deployed embodied AI robots. Zoomlion will continue advancing its embodied AI technologies and scaling humanoid robot deployments across industrial manufacturing, specialized operations and smart services. Stay Curious. Stay Inspired. Discover Incredible Things. © 2026 All Rights Reserved Stay Curious. Stay Inspired. Discover Incredible Things.Incredible Things © 2026 All Rights Reserved
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| Zoomlion's Full-Stack Humanoid Robots Compete at World Humanoid Robot Games | https://www.manilatimes.net/2026/09/16/… | 0 | Sep 18, 2026 16:00 | active | |
Zoomlion's Full-Stack Humanoid Robots Compete at World Humanoid Robot GamesDescription: BEIJING, Sept. 16, 2026 /PRNewswire/ -- Zoomlion Heavy Industry Science & Technology Co., Ltd. ('Zoomlion') recently fielded its independently developed humanoi... Content: |
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| China trains humanoid robots for combat | https://www.azernews.az/region/263575.h… | 4 | Sep 18, 2026 16:00 | active | |
China trains humanoid robots for combatURL: https://www.azernews.az/region/263575.html Description: China has emerged as the global leader in the development and production of humanoid robots, strengthening its position in one of the world’s fastest-growing technology sectors. Content:
by Alimat Aliyeva China has emerged as the global leader in the development and production of humanoid robots, strengthening its position in one of the world’s fastest-growing technology sectors. This was stated in a report published by Bank of America. According to the report, China accounted for around 95% of global humanoid robot shipments in 2025. Chinese researchers and technology companies are actively developing these machines as part of the country’s broader efforts to modernize its economy and strengthen its technological capabilities. At the same time, China’s People’s Liberation Army (PLA) has begun exploring potential military applications for humanoid robots, including their use in combat environments. An analysis of more than 100 military procurement notices, scientific studies, patents, government publications and documents from defense companies indicates that Chinese military institutions are testing how humanoid robots could be integrated into combat operations. Researchers are also acquiring the robots and related technologies, developing training methods and studying how the machines could interact with human soldiers. This research accelerated significantly in 2025–2026, with particular attention being paid to environmental perception, object manipulation and the collection of training data. These capabilities are considered essential for robots operating in complex and unpredictable environments. So far, there is no indication that humanoid combat robots have been deployed with active PLA units. However, Chinese researchers have already proposed concepts for using such machines in urban warfare, where their human-like design could allow them to navigate buildings, stairs and other environments designed for people. The rapid development of humanoid robots could therefore have implications far beyond civilian industries. If the technology continues to mature, these machines could eventually be used for logistics, reconnaissance, hazardous operations and other military tasks where reducing risks to human personnel would be a major advantage. Here we are to serve you with news right now. It does not cost much, but worth your attention. Choose to support open, independent, quality journalism and subscribe on a monthly basis. By subscribing to our online newspaper, you can have full digital access to all news, analysis, and much more. You can also follow AzerNEWS on Twitter @AzerNewsAz or Facebook @AzerNewsNewspaper Thank you! © Azernews.az 2026
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| China humanoid robots military: From dance floor to war: China … | https://economictimes.indiatimes.com/te… | 10 | Sep 18, 2026 16:00 | active | |
China humanoid robots military: From dance floor to war: China readies humanoid robots for combat - The Economic TimesDescription: Two days after the Games ended, the People's Liberation Army's official newspaper drew its âown conclusions from the show. The PLA Daily called for researchers to accelerate the transfer of cutting-edge technologies from âlaboratories to military training grounds for robotic "combatants." Content:
Listen to this article in summarized format (Catch all the Technology News News, and Latest News Updates on The Economic Times.) ...more Popular Categories Hot on Web In Case you missed it Top Searched Companies Other useful Links Top Calculators Top Slideshow Top Story Listing Top Prime Articles Top Definitions Top Commodities Private Companies Top Market Pages Latest News follow us on Download ET App:
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| Datasea Expands into China's Trillion-RMB Silver Economy Through Strategic Cooperation … | https://www.manilatimes.net/2026/09/16/… | 0 | Sep 17, 2026 16:00 | active | |
Datasea Expands into China's Trillion-RMB Silver Economy Through Strategic Cooperation in AI Elderly Care RobotsDescription: **media[1220540]**Datasea plans to combine its AI agent and digital platform capabilities with its partner's robotics technologies and elderly care deployment e... Content: |
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| Xiaomi Says CyberOne Hit 98% in Factory Trial | https://www.techrepublic.com/article/ne… | 0 | Sep 17, 2026 08:00 | active | |
Xiaomi Says CyberOne Hit 98% in Factory TrialURL: https://www.techrepublic.com/article/news-xiaomi-cyberone-factory-trial-apac-china/ Description: Xiaomi showcased CyberOne at IFA after reporting a 98% success rate in an EV factory trial, highlighting the promise and limits of humanoid robots. Content: |
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| Chinese humanoid robots face challenge of their own capabilities | https://www.cnbc.com/2026/08/21/chinese… | 10 | Sep 17, 2026 08:00 | active | |
Chinese humanoid robots face challenge of their own capabilitiesDescription: Humanoid robots still struggle to perform as efficiently as humans in most labor scenarios. Content:
BEIJING — The big challenge for humanoid robots is still getting the technology to work well, according to industry leaders speaking alongside the World Robot Conference in Beijing this week. Robots are not yet as efficient as humans, and take time to learn new skills, creating a bottleneck for the industry, Unitree's founder Wang Xingxing said, addressing the conference one day after his company's 460% IPO-day surge. His remarks underscored the challenges for humanoid robots entering the human workforce. Shares of Unitree fell 18.7% on Thursday. The U.S. Federal Communications Commission last month added foreign-made advanced robotic devices, including humanoids, to a list restricting imports to the U.S. The statement did not specify a country, and said retailers could still import models the FCC has previously approved. But the impact of that limitation isn't that great right now because there aren't that many humanoids being used in the U.S., Jeff Burnstein, president of the Association for Advancing Automation, told CNBC. "Where the impact might be in the U.S. is on autonomous mobile robots, which are used in factories that much more than humanoids are," he said. The state of technology means companies deploying robots to increase efficiency aren't even focused on humanoids right now. "What I hear from customers in the U.S. [is that] 'we want solutions. We have a problem. We need a solution. We don't care if it's a humanoid. We don't care if it's a traditional robot, a collaborative robot. We don't even care if it's a robot. We need a solution," Burnstein said. "So the onus is on the humanoid players to show we have a solution," he said, noting the robots need to be affordable, safe and ready to use. Keenon, a startup, develops humanoids to use in conjunction with simpler delivery robots to handle laundry services in hotels, for example, according to COO Wan Bin. Keenon's business partners include Buffalo Wild Wings and Hilton. Completing 50% of a task well is relatively easy, even 80%, he said. But to reach a 99.9% completion rate really tests engineering and training capabilities, Wan said. He said the company has shipped more than 100,000 robots, and expects that to exceed 150,000 units by the end of next year. Got a confidential news tip? We want to hear from you. Sign up for free newsletters and get more CNBC delivered to your inbox Get this delivered to your inbox, and more info about our products and services. © 2026 Versant Media, LLC. All Rights Reserved. A Versant Media Company. Data is a real-time snapshot *Data is delayed at least 15 minutes. Global Business and Financial News, Stock Quotes, and Market Data and Analysis. Data also provided by
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| UK49s Lunchtime & Teatime Results Today 49s Result 2026 UK49 | https://profitconfidential.com/chinese-… | 10 | Sep 17, 2026 08:00 | active | |
UK49s Lunchtime & Teatime Results Today 49s Result 2026 UK49Description: UK49s Lunchtime & Teatime Results for today 2026 UK49 lunch & tea result. 49s history, latest UK 49's winning numbers & past draws Content:
The Official 49s lottery draw and UK49 results for 2026 are published here. There are now 4 lottery draws held every day and you can find the results of each draw on this website. The Lunchtime and Teatime results of the day are available on this page. Click Here to see the Brunch and Drivetime winning numbers. Also check yesterday’s draw outcomes and past UK 49s results history. 49s results for the UK and South Africa are available on our website. You can easily see today’s outcomes and also look at older winning numbers all in one spot. You can scroll down the page to have a look at the hot and cold numbers of the day in order to make your predictions. Also click the past results link to the see previous 49s draw outcomes and history. The lunchtime result is a set of winning numbers from the afternoon UK 49s draw. They are updated daily at around 2:49 PM (South African time).You can also view previous winning numbers on our Lunchtime Results History page to help track patterns and improve your picks. The UK49s lunchtime draw is one of the most popular daily betting events for punters. Like the name suggests, it is held earlier in the day between late morning and early afternoon. It often offers a perfect midday opportunity to turn a small stake into a massive payout. In South Africa, the winning numbers are typically announced at 14:49 SAST during the summer months and 13:49 SAST during the winter. The teatime results show the winning numbers from the evening UK49s draw, published daily at around 7:49 PM (South African time), including weekends.Check out our Teatime Results History page to explore past draws and find your lucky numbers. This is the last opportunity of the day for South African players to win big playing the UK49s lottery. Most 49s enthusiasts take the teatime draws more seriously, taking all day to forecast and predict the outcome. In South Africa, this lotto typically takes place at 19:49 SAST during summer and 18:49 SAST during winter and the teatime result is published immediately after. The timing of the draw often coincides with the end of the workday, which allows so many who may missed the earlier draws to participate. Whether you’re a seasoned 49s lottery pro or a beginner looking for your first big win, the UK 49s Lunchtime and Teatime draws are a staple in South Africa’s betting scene. Because these draws happen twice a day, every day, they offer more excitement than your standard weekly lotto. Below is your ultimate guide to the lottery, tailored specifically for all punters. It is a daily “Lucky Numbers” draw that is incredibly popular across South Africa. Unlike the national lotto, which only draws on specific days, the 49s draw gives you two chances to win every single day. It is a 6/49 draw, meaning 6 main balls and 1 “Booster Ball” are drawn from a pool of 1 to 49. Local players usually search for results shortly after the draws take place. You can find the latest winning numbers on this page. In South Africa, the draw times vary slightly depending on UK Daylight Savings Time Lunchtime Draw: Usually around 14:49 SAST (Summer) or 13:49 SAST (Winter). Teatime Draw: Usually around 19:49 SAST (Summer) or 18:49 SAST (Winter). For South African lotto players the lunchtime result and teatime result are released immediately after their draws are held and can be seen on this website at their respective time slot. Playing the 49s lotto is simple, but the best part is the flexibility. In South Africa, you don’t just “buy a ticket”you “bet” on the numbers through a bookmaker. Choose Your Market: You can bet on up to 6 numbers. You can also choose to include the Booster Ball to increase your odds. Pick Your Numbers: Select any numbers from 1 to 49. Many South Africans use “hot and cold” number strategies or pick based on special dates like birthdays. Set Your Stake: Unlike a fixed ticket price, you decide how much you want to bet (e.g., R5, R10, or R50).11 Confirm the Draw: Choose whether you are playing for the Lunchtime draw, the Teatime draw, or both! To play, you simply select your lucky numbers from a pool of 1 to 49. While the draw involves six main balls and a Booster Ball, the beauty of playing is the flexibility. You can choose to bet on a single number or try to match more for higher odds. If you successfully match all your chosen numbers with the official UK 49s lunchtime results, you could be looking at a life-changing windfall. Even matching just two or three numbers offers great returns, making it a favorite for those who play daily. You can easily join the action by placing your bets through major local bookmakers or online betting platforms. One of the reasons the UK49s is so popular is the fixed odds. You know exactly how much you will win based on your stake. While payouts vary slightly between bookmakers (like Hollywoodbets and Bet365), here is a general idea of what you can expect: Here is the typical payout structure for a R10 bet at most South African bookmakers: 1 Number Matched Typical Odds: 7/1 Winning Return: R80 2 Numbers Matched Typical Odds: 65/1 Winning Return: R660 3 Numbers Matched Typical Odds: 715/1 Winning Return: R7,160 4 Numbers Matched Typical Odds: 10,000/1 Winning Return: R100,010 Bonus/Booster Ball Only Typical Odds: 45/1 Winning Return: R460 Pro Tip: If you “accurately predict” even just 2 or 3 numbers, your return on investment is significantly higher than most other lottery games. You don’t need to hit the “jackpot” to walk away with a massive cash prize. The UK lunch draw, which is organized by the 49’s company is held late morning or early afternoon, depending on the season. As with most events in South Africa, daylight savings plays a role in the timing. It holds at 2:49 PM(South African Time). The results of the lotto game are published just after the winning numbers are announced. In the afternoon draw, balls from 1 to 49 are mixed together and automatically drawn by a reputable machine. And after, 6 numbers and 1 booster ball (also known as the bonus ball) are picked. This becomes the result of the lottery. You can play this UK49 game at several betting shops in South Africa or on various sites that offer 49s lotto. 49’s is the organizer of the UK49s evening lottery and it holds the draw everyday of the week, from Monday to Sunday, with no breaks apart from Christmas day. Being the late draw, it entertains more players than other 49s lotto game. This holds at 19:49 PM(South African Time), hence, it is known as the Teatime Draw. In the tea draw, balls from 1 to 49 are drawn. And after the draw, 6 numbers and 1 booster ball (also known as the bonus ball) are picked. This becomes the result of the draw. A player can play the game at several betting shops in South Africa or on various sites that offer 49’s lottery. The UK49s draw originated in 1996, launched by 49s Ltd, a consortium of major UK bookmakers. This happened after the anticipated debut of the National Lottery. Unlike traditional lotteries, it was designed as a fixed-odds lottery product for betting shops. The game initially featured both Lunchtime and Teatime draws to provide frequent daily opportunities for players to select numbers from a 1 to 49 pool. In 2026, 2 more draws were added to make it a total of 4 daily draws. Its unique structure allows players to choose how many numbers to bet on, with payouts remaining fixed rather than shared. The Booster Ball was included from the start to offer varied prize tiers. While the draws occur in the UK, the game has become a massive cultural phenomenon in South Africa (Mzansi), where it is the gold standard for “Lucky Numbers” betting. Throughout its history, the game has been defined by its transparency and consistency. Punters globally track “hot and cold” patterns, yet the draws remain entirely random. Today, UK49s persists as a dominant global search term and the most popular lottery draw in South Africa, maintaining the same reliable format it established three decades ago. Welcome to UK49s Today, your best source for the latest and most accurate UK49s Result on the internet. We are dedicated to providing our visitors with the lunchtime results and teatime results of the 49s draw. Our team is passionate about the UK49 lottery and understands the importance of having the results delivered fast to you. That’s why we work tirelessly to ensure that our website is always updated with the latest results, ensuring that you never miss out on any draw. Copyright 2026 © All rights Reserved.
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| Xiaomi may soon launch AI robots, CEO shares video showing … | https://www.indiatoday.in/technology/ne… | 10 | Sep 17, 2026 08:00 | active | |
Xiaomi may soon launch AI robots, CEO shares video showing humanoids working in factory - India TodayDescription: Could Xiaomi's next big product announcement be an AI robot? A new video shared by CEO Lei Jun shows the company's humanoid robots working inside a factory, hinting at how Xiaomi is steadily expanding its robotics ambitions beyond smartphones and EVs. Content:
Xiaomi is already doing big business with its smartphones, smart home devices and electric vehicles, but we may soon see AI-powered robots added to that list. The company's CEO, Lei Jun, recently shared a behind-the-scenes video showing the company's humanoid robots carrying out tasks on an automotive production line, offering a glimpse of how Xiaomi is testing the robots in real factory settings. And this might be the hint towards bigger plans for Xiaomi robotics business. In a recent post on X, Lei shared an uncut video showing Xiaomi's humanoid robot continuously sorting centre console side covers on an automotive production line. "One of the toughest challenges for robots in factories is handling large, irregular, flexible parts reliably over long periods,” writes Jun. “Here is an uncut video of Xiaomi's humanoid robot continuously sorting centre console side covers on the production line." Watch the video below: One of the toughest challenges for robots in factories is handling large, irregular, flexible parts reliably over long periods.Here is an uncut video of Xiaomi's humanoid robot continuously sorting center console side covers on the production line. pic.twitter.com/Uv10XZ5uUF— Lei Jun (@leijun) July 15, 2026 One of the toughest challenges for robots in factories is handling large, irregular, flexible parts reliably over long periods.Here is an uncut video of Xiaomi's humanoid robot continuously sorting center console side covers on the production line. pic.twitter.com/Uv10XZ5uUF— Lei Jun (@leijun) July 15, 2026 The footage shows the Xiaomi-branded humanoid robot picking up the automotive components and placing them into designated bins, while another wheeled robot with a humanoid upper body can also be seen moving around the factory floor in the background. While Xiaomi has not announced plans to commercially launch its humanoid robots, the latest video suggests the company is continuing to expand real-world testing of the technology inside its own manufacturing facilities. Xiaomi's first robot Xiaomi first entered the humanoid robotics space in 2022 and unveiled CyberOne, a full-sized humanoid robot capable of recognising people, perceiving its surroundings and performing simple interactions. At the time, the company described CyberOne as an important step in its robotics ambitions, although it made it clear that the project was still in the research and development stage rather than a commercial product.Since then, Xiaomi has steadily moved from showcasing the robot on stage to testing it in industrial environments. Earlier this year, the company also revealed that its self-developed humanoid robots had successfully completed three continuous hours of autonomous work inside its electric vehicle factory without human intervention. According to Xiaomi, the robots carried out a range of automotive assembly tasks, including tightening screw nuts, installing vehicle badges, removing protective films, picking and placing self-tapping nuts, and transporting material boxes. The company said that factory trials marked the first step towards the stable use of its humanoid robots in intelligent manufacturing. At the time, Lei also revealed that Xiaomi plans to deploy "a large number" of self-developed humanoid robots across its factories over the next five years, although the company did not disclose how many robots it intends to use or when the wider rollout will begin. - EndsPublished By: Divya BhatiPublished On: Jul 15, 2026 17:19 ISTAlso Read | YouTube and X are directing millions of users to nudify apps, says studyAlso Read | ChatGPT-5.6 Sol is deleting files on its own and making developers angryAlso Read | Google DeepMind CEO warns AGI is coming, wants frontier AI models checked by US standards body before launch Meet Sethuraman Panchanathan, the Chennai-born scientist behind a historic NAE honour | Monsoon's final push: Satellite captures thunderstorms roaring over North India | Girl goes missing while sleeping, found dead in water tank with hands, feet tied | Japan PM Sanae Takaichi reshuffles Cabinet but keeps key US-facing ministers | TMC Symbol Battle: Rebel Faction & Mamata Camp To Meet Election Commission | SRK performs aarti at Ambani Ganpati celebration after quiet entry. 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| Machine Learning Meets MINDSTORMS | http://www.blogger.com/feeds/21402852/p… | 0 | Sep 16, 2026 08:00 | active | |
Machine Learning Meets MINDSTORMSURL: http://www.blogger.com/feeds/21402852/posts/default/5375416215689516071 Content: |
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| Chinese firm releases 11,430 robot trajectories furthering research | https://interestingengineering.com/ai-r… | 10 | Sep 16, 2026 08:00 | active | |
Chinese firm releases 11,430 robot trajectories furthering researchDescription: AGIBOT open-sources a real-world RL dataset capturing robot successes, failures and human intervention to advance embodied AI. Content:
Pick the engineering stories that matter and get them in your inbox. Access expert insights, exclusive content, and a deeper dive into engineering and innovation all with fewer ads or a completely ad-free experience. All Rights Reserved, IE Media, Inc. Follow Us On Access expert insights, exclusive content, and a deeper dive into engineering and innovation all with fewer ads or a completely ad-free experience. All Rights Reserved, IE Media, Inc. The dataset spans numerous trajectories across 14 tasks, capturing robot successes, failures, autonomous runs, and human interventions. Chinese robotics player AGIBOT has open-sourced its WORLD 2026 dataset, which focuses on reinforcement learning for embodied AI. The dataset is designed to help robots learn from real-world interactions rather than relying solely on successful expert demonstrations. It captures the broader learning process, including how robots improve through failures and human intervention. Recently, AGIBOT topped the second World Humanoid Robot Games with 46 medals, including 18 golds, making its international competition debut. AGIBOT’s WORLD 2026 Theme 3 dataset initiative provides researchers with real-world robot experience data aimed at advancing reinforcement learning and embodied AI. The open-source dataset contains 11,430 real-world trajectories collected across 14 industrial and household tasks. Unlike datasets focused primarily on successful demonstrations, the new release captures a wider range of robot experiences, including successful executions, failed attempts, autonomous deployments, and interventions by human operators. The dataset is organized around three types of trajectories. The first consists of expert demonstrations, which document reference task executions carried out by human operators in real-world environments. These demonstrations provide robots with examples of how tasks can be performed and serve as a foundation for learning. The second category covers autonomous policy rollouts. These records capture robots attempting tasks independently after deploying learned policies. Both successful and unsuccessful attempts are included, allowing researchers to examine not only what a robot can accomplish but also where and why its performance breaks down. The dataset includes 1,024 successful policy rollouts and 1,369 failed rollouts. The third category focuses on human-in-the-loop corrections. These trajectories document a robot’s behavior before an intervention, the moment a human operator takes control, and the corrective actions used to recover or complete the task. This provides researchers with data on how robots respond when autonomous behavior goes wrong and how human guidance can help overcome failures. AGIBOT has also included detailed annotations covering task progress, completion status, errors, environmental disturbances, and human interventions. Such information is intended to give researchers a more structured view of robot behavior and provide useful signals for reinforcement learning and other embodied AI applications. The release reflects a broader shift in robot learning from relying primarily on curated demonstrations toward learning from actual deployment experience. In real-world environments, robots can encounter unexpected obstacles, make mistakes, deviate from intended actions, or require assistance. Capturing these situations can help researchers study capability limits, failure modes, and recovery strategies that successful demonstrations alone may not reveal. Theme 3 forms part of the broader AGIBOT WORLD 2026 initiative, which is being developed as an open-source resource for embodied AI research. The company plans to expand the initiative with additional datasets, benchmarks, and research resources. According to the firm, by making real-world successes, failures, and corrective interactions available to researchers, it aims to provide a broader foundation for developing robots capable of learning continuously from their own experiences and operating more reliably in everyday environments. “We invite researchers worldwide to leverage AGIBOT WORLD 2026 to drive robotic intelligence from the lab into the real world, empowering every industry and tangibly boosting production and service efficiency,” reads the AGIBOT WORLD website. Jijo is an automotive and business journalist based in India. Armed with a BA in History (Honors) from St. Stephen's College, Delhi University, and a PG diploma in Journalism from the Indian Institute of Mass Communication, Delhi, he has worked for news agencies, national newspapers, and automotive magazines. In his spare time, he likes to go off-roading, engage in political discourse, travel, and teach languages. Premium Follow
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| Agility Robotics : le robot Digit entre en bourse au … | https://www.jbmm.fr/actu2644/agility-ro… | 8 | Sep 12, 2026 16:00 | active | |
Agility Robotics : le robot Digit entre en bourse au NasdaqURL: https://www.jbmm.fr/actu2644/agility-robotics-digit-entree-bourse-nasdaq/ Description: Agility Robotics, fabricant du robot humanoïde Digit déjà déployé chez GXO et Toyota, va entrer en bourse au Nasdaq via une fusion SPAC valorisée 2,5 milliards de dollars. Content:
Date: Share: Agility Robotics, l’entreprise américaine derrière le robot humanoïde Digit, s’apprête à devenir la première société cotée en bourse entièrement dédiée aux robots humanoïdes. L’opération se fait via une fusion avec un SPAC, une valorisation de 2,5 milliards de dollars et un futur ticker au Nasdaq. Sommaire Agility Robotics va fusionner avec Churchill Capital Corp XI, un véhicule d’acquisition créé par le financier Michael Klein. L’opération valorise la startup à environ 2,5 milliards de dollars, pour des produits attendus supérieurs à 600 millions de dollars : 420 millions en liquidités apportées par le SPAC, complétés par plus de 200 millions issus d’un placement privé mené par Foxconn. Une fois la fusion bouclée, l’action doit s’échanger au Nasdaq sous le ticker AGLT. L’entreprise est dirigée par Peggy Johnson, ancienne cadre de Microsoft et Magic Leap, et la transaction reste soumise à l’accord des actionnaires et de la SEC d’ici la fin de l’année. Contrairement à de nombreux prototypes encore cantonnés aux vitrines de salons, Digit fonctionne déjà en conditions réelles : l’entreprise revendique plus de 65 000 heures de fonctionnement cumulées sur neuf sites clients, dont GXO, Schaeffler, Toyota Motor Manufacturing Canada et Mercado Libre. Chez GXO, le robot a notamment dépassé les 100 000 bacs déplacés dans un centre de distribution, une preuve de fiabilité sur des tâches répétitives comme le tri ou le chargement de convoyeurs. La France n’est pas en reste sur ce terrain, avec par exemple le robot humanoïde industriel Northstar, ou encore l’ouverture encadrée des routes aux robots de livraison autonomes. Les documents déposés en vue de la fusion montrent une entreprise encore largement déficitaire : les dépenses opérationnelles sont passées d’environ 71 millions de dollars en 2024 à 111 millions en 2025, pour une consommation de trésorerie proche de 100 millions de dollars sur la période. Agility met en avant plus de 300 millions de dollars de commandes « engagées » pour la prochaine génération Digit v5, mais ce chiffre dépend en réalité de la réalisation de certains jalons, et provient pour l’essentiel d’un contrat unique de trois ans portant sur 1 000 robots, avec un client dont l’identité n’a pas été révélée. Un pari qui rappelle d’autres paris logistiques coûteux, comme l’expansion massive de la livraison par drone d’Amazon. La fusion avec Churchill Capital Corp XI permet à Agility Robotics d’accéder rapidement aux marchés financiers pour lever des fonds, sans passer par une introduction en bourse classique. Digit est déployé chez GXO, Schaeffler, Toyota Motor Manufacturing Canada et Mercado Libre, principalement pour des tâches de manutention en entrepôt. Non, l’entreprise reste déficitaire, avec des dépenses opérationnelles en forte hausse et une consommation de trésorerie proche de 100 millions de dollars en 2025. Cet article a été rédigé avec l’aide d’une intelligence artificielle. Politique éditoriale JBMM.fr est un magazine dédié à l'informatique et aux nouvelles technologies. Actualité High-Tech, Conseils professionnels et Guides d'achat autour du numérique. © JBMM.fr - Tous droits réservés Politique éditoriale et usage de l’intelligence artificielle
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| China's Unitree says "GPT moment" for robots remains years away | https://kr-asia.com/chinas-unitree-says… | 2 | Sep 12, 2026 08:00 | active | |
China's Unitree says "GPT moment" for robots remains years awayURL: https://kr-asia.com/chinas-unitree-says-gpt-moment-for-robots-remains-years-away Description: The robotics company intends to allocate nearly half of its IPO proceeds to embodied intelligence research. Content:
Written by Nikkei Asia Published on 28 Jul 2026 3 mins read Robotics may still be two to five years away from a “GPT moment” comparable with ChatGPT’s breakthrough in generative artificial intelligence, a senior executive at one of the leading Chinese humanoid makers said on July 22. Irving Chen, director for the Asia Pacific and other areas at Unitree Robotics, said large language models (LLMs) had demonstrated their abilities in software but remained poorly connected to robots and machines that need to operate in the physical world. “The LLM is powerful in the virtual world,” Chen told Nikkei‘s Global Digital Summit 2026 in Tokyo, but it had yet to show the same capability when controlling hardware, which needs to process more variables to operate in different environments. Chen’s comments highlight the challenges facing the booming humanoid robotics industry. Robots are expected to bring autonomous physical AI into factories, but most remain confined to entertainment and demonstrations based on preset movements. The Hangzhou-based startup has become one of the most high-profile companies in the humanoid robotics space, with its viral synchronized dances and martial arts displays in China. Chen said the performances show that the hardware is becoming reliable and scalable, but has not yet demonstrated intelligence suited to productive work. For Unitree, commercial demand remains concentrated in research and testing. In the first nine months of last year, 74% of its humanoid robot revenue came from research and education, with just 9% from industrial customers. Chen said wider adoption would require robots to perform longer and more complex tasks reliably in changing environments without pre-programming. “Right now, most robotics projects are in the pilot stage,” he said. Unitree expects proof-of-concept trials to dominate through next year, followed by commercial deployment by 2030. From then on, robots could begin assisting humans with a broader range of general labor tasks, Chen said. He identified more capable robot “brains” and large-scale manufacturing for the industry’s next breakthrough. Software models need to interpret their surroundings and turn instructions into physical actions, with faster computing response times necessary. Manufacturers have to produce durable machines at lower cost and in larger volumes. Unitree, founded in 2016, is preparing for an IPO in Shanghai after receiving regulatory approval earlier this month. It plans to raise RMB 4.2 billion (USD 619.7 million), with 48% allocated to AI and embodied intelligence research. A further RMB 1.1 billion (USD 162.3 million), or over 26%, would go toward newer hardware development, while RMB 624 million (USD 92.1 million), close to 15%, would be used for new factories with capacity to produce around 20,000 robots a year. The company shipped 6,500 units last year. He added that the company had increased investment in embodied intelligence, which refers to the integration of AI into physical systems, and plans to launch more robotics models designed to work across homes, offices and industrial sites without pre-setting. “Our expectation is one model that can fit most scenarios,” Chen said. This article first appeared on Nikkei Asia. It has been republished here as part of 36Kr’s ongoing partnership with Nikkei. Note: RMB figures are converted to USD at rates of RMB 6.78 = USD 1 based on estimates as of July 27, 2026, unless otherwise stated. USD conversions are presented for ease of reference and may not fully match prevailing exchange rates. Loading... Subscribe to our newsletters KrASIA A digital media company reporting on China's tech and business pulse.
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| Ai Humanoid Robots Google Deepmind Meta Nvidia Tech - Quartz | https://qz.com/ai-humanoid-robots-googl… | 4 | Sep 12, 2026 08:00 | active | |
Ai Humanoid Robots Google Deepmind Meta Nvidia Tech - QuartzURL: https://qz.com/ai-humanoid-robots-google-deepmind-meta-nvidia-tech-1851771214 Description: The technology for humanoid robots is advancing. But a fundamental question remains Content:
Free daily briefing on global business news. Global business news for a smarter world © 2026 Quartz Media, Inc. All rights reserved. The technology for humanoid robots is advancing. But a fundamental question remains A version of this article originally appeared in Quartz’s members-only Weekend Brief newsletter. Quartz members get access to exclusive newsletters and more. Sign up here. In a demonstration video from Google $GOOGL DeepMind, a robot arm delicately folds origami, packs snacks into Ziploc bags, and deftly manipulates objects with surprising precision. When an item slips from its grasp, the robot quickly readjusts and continues its task. These aren’t jerky, pre-programmed movements, but something more fluid and adaptive — the result of a new AI model called Gemini Robotics that the company unveiled earlier this month. Join 500,000+ readers who start their day with Quartz. By subscribing, you agree to our Terms of Service and Privacy Policy. While the showcase focuses on robotic arms rather than full humanoid robots, the underlying technology is the same that will power the next generation of human-shaped machines. Google says its Gemini Robotics model is designed to “easily adapt to different robot types” and is already being tested with Apptronik’s humanoid Apollo robot. “In order for AI to be useful and helpful to people in the physical realm, they have to demonstrate ‘embodied’ reasoning — the humanlike ability to comprehend and react to the world around us,” Carolina Parada, who leads the DeepMind robotics team, said in a statement. The demonstration is part of a new wave of humanoid robots from tech giants like Google and Meta $META, and startups like Figure AI and Agility Robotics. They’re being pitched as the future of logistics and household chores. Perhaps no science fiction technology besides flying cars has tantalized us so much and for so long as the promise of robot helpers that would finally free us from the drudgery of dishes and laundry (OK, these stories have also scared us on occasion, too). Now, equipped with advanced AI models, these mechanical workers are taking their first tentative steps out of our imagination and into reality. But while the technology is advancing, a fundamental question remains: Should we build robots for our world, or adapt our spaces for simpler machines? The makers of these humanoid robots are pushing for the former. They argue the world is designed for human bodies, after all, with stairs, shelves at shoulder height, and important things located at eye level. Humanoid robot advocates argue this makes the human form the most logical design for machines meant to integrate into existing environments like our kitchens. They are fighting an uphill battle against the only successful robots so far, which are mostly non-humanoid robots in warehouses, where shelving systems are designed for wheeled picking robots or roped off areas that are only for robots. These purpose-built environments allow for much simpler robot designs. But humanoid robotics companies have a powerful new tool they’re betting will change everything: AI systems like Google’s Gemini and OpenAI’s GPT that understand and generate human speech. This technology could let people simply talk to robots like they’d talk to a person — “fold that shirt,” or “put away the dishes” — without needing specialized programming or technical knowledge. Even more promising, these AI models might help robots adapt to new situations they weren’t specifically trained for, potentially solving one of robotics’ most persistent challenges. Despite impressive demos and promises to high heaven, the current reality is more modest. The robots still remain slow to us by comparison, and struggle with delicate or malleable items that change shape when grasped. The unpredictable chaos of a household with young children running around, toys scattered across the floor, or unexpected situations like finding keys in the refrigerator — what could be a regular Tuesday in many households — remain largely untested scenarios far beyond current capabilities. These problems aren’t slowing down companies from at least trying. Meta is reportedly building a platform for humanoid robots that would be “the Android of androids.” Elon Musk, already stretched thin on his many, many, projects, has found time to keep posting about Tesla $TSLA’s Optimus humanoid robot. He recently announced on X $TWTR that at least one of his bots will be on its way to Mars by “end of next year,” beating humans out by at least a couple of years. But other significant barriers remain before these robots enter widespread use. Researchers in human-robot interaction have observed that humans typically have much lower tolerance for robot errors than for human ones. Studies in this field show that while we might forgive a human coworker who occasionally drops items, a robot that makes even a single significant mistake can permanently lose user trust. This trust issue becomes even more complicated as robots integrate large language models, which are known to occasionally “hallucinate,” or generate incorrect information. A robot that confidently misinterprets a command due to an LLM hallucination could create dangerous situations in physical environments. While an AI chatbot’s mistake might be merely frustrating, a robot acting on hallucinated instructions could damage property or injure people. Regardless, billions of dollars continue to flow into humanoid robotics from tech leaders who grew up immersed in science fiction and don’t want to give up the dream. At Nvidia $NVDA’s annual developer conference this week, CEO Jensen Huang showed off new software that he said would help humanoid robots to more easily move through our spaces. When asked later when we will know that AI has become ubiquitous, he said it would be when humanoid robots are “wandering around.” And he said that’s coming soon. “This is not five-years-away problem,” he said. “This is a few-years-away problem.”
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| The New World Of AI Robots – Voice of the … | https://voiceofthedba.com/2026/08/22/th… | 9 | Sep 12, 2026 08:00 | active | |
The New World Of AI Robots – Voice of the DBAURL: https://voiceofthedba.com/2026/08/22/the-new-world-of-ai-robots/ Description: It's been a good time for robots. While on vacation last week I caught this video of a robot running. It's impressive for a bit, and then it devolves into the inspiration for lots humorous comments as it crashes into a wall. There are some funny comments, but some pedantic ones that note the robot… Content:
Voice of the DBA · It’s been a good time for robots. While on vacation last week I caught this video of a robot running. It’s impressive for a bit, and then it devolves into the inspiration for lots humorous comments as it crashes into a wall. There are some funny comments, but some pedantic ones that note the robot can’t beat a human record because it’s not a human. That’s correct, but … The comments miss the point. The robot will improve it’s capabilities, and much faster than a human can. The AI/ML advances of the last few years mean that we don’t have to program robots to be precise and exact. The fact that a robot can balance and run and stay inside the lanes is incredible. I suspect these robots, both humanoid and other form factors will start to become more commonplace in our world. Which is scary. Not for us tech people, though certainly AI advances are worrisome. More, I worry about other jobs. Think about the industrial robots of the last 30 years that have been used in places like car manufacturing. They are bespoke, designed to do certain jobs, and in a certain way. They are programmed with fairly tight tolerances to perform a specific task, often at a quicker and more reliable (and repeatable) way than a human can. There were plenty of false starts here, but today many robots are used alongside humans to assemble cars. You can see them working here, doing tasks that would be slower and harder for humans, even with mechanical aids. There is talk of humanoid robots being used in place of some humans, reducing the slow and complex setup . This also lets the robots work in the same places and spaces, moving the same way, as humans do. This might reduce some of the labor costs in the future. That might not seem like a big change overall, as lots of manufacturing uses automation in some way today, but think past this. You can purchase a humanoid robot for under $5000. That might not be very capable now, but as LLMs get more capable and perhaps specialized models for different purposes like image recognition, this is an issue. Imagine you own an oil change business. You pay 5 people to do most of the work on cars. Those people likely cost you $2000-2500 a month each. That’s the cost of 2 robots, without the hassles of hiring, termination, breaks, etc. An AI LLM can already identify items from a camera image. Is it a far stretch to think that a robot could identify the oil drain plug and the oil filter on a car by moving around it? How hard would it be for a robot to grab a human ratchet, pick the right socket after a database lookup, and remove the plug. They could tell when the oil finished draining and then replace the plug, tightening it to the correct torque. And being a robot, they might do this without forgetting to position the drain or replace the plug. In my mind, a $5k robot quickly becomes capable of a lot of human jobs. There might still be the need for some humans, but we might easily replace 50%+ of them in a lot of common jobs. Stocking shelves, acting as cashiers, who knows what else these AI driven systems might accomplish. That’s truly a scary world, where human labor in many cases might be devalued. In the software world, it seems the people having the most success have the best judgment. People who are above average software engineers get above average results from LLMs, and I suspect this will be the case for a long time. Very average, or worse, engineers get worse results and I think are the source of many of the stories of AI coding failures. I don’t know what a lot of manual labor jobs will do when management starts to experiment with robots, but I know that in our world you can compete and succeed against AI coding agents by learning to work with them, apply your judgment and use them as tools that make you more effective. Steve Jones administration AI Azure Backup/Recovery career career2 car update Cloud Computing coping database design databases Database Weekly DevOps disaster recovery encryption Friday Poll goals hardware Humor life Microsoft misc PASS powershell Redgate republish sabbatical security software development software development speaking SQL in the City SQLNewBlogger SQL Prompt SQL Saturday sql server syndicated T-SQL T-SQL Tuesday Tesla travel vacation windows words work anything that can move that fast presents both actual danger and with raise our threat warning system LikeLike Δ This site uses Akismet to reduce spam. Learn how your comment data is processed. Receive our articles in your inbox. Twenty Twenty-Five Blog at WordPress.com.
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| Chinese humanoid robots smash human records at Beijing robot games … | https://www.business-standard.com/world… | 10 | Sep 11, 2026 16:00 | active | |
Chinese humanoid robots smash human records at Beijing robot games | World News - Business StandardDescription: More than 2,000 humanoid robots were participating in the event Content:
Booster Robotics humanoid robots walk in formation during the opening ceremony for the second World Humanoid Robot Games at the National Speed Skating Oval in Beijing, China August 22, 2026 | REUTERS Chinese humanoid robots broke records set by humans, including beating Usain Bolt's 100-meter sprint world record, on the opening day of the Olympics-like World Humanoid Robot Games in Beijing. More than 2,000 humanoid robots were participating in the event, the organizer said. The five-day games, now in its second year, are a spectacle demonstrating China's rapid progress in advanced robotics as the technology race with the U.S. heats up, with 51 events and more than 1,000 competitions taking place including running, table tennis and soccer. The games, which are taking place in the National Speed Skating Oval built for the 2022 Winter Olympics, opened the same week as Beijing held the 2026 World Robot Conference, where companies showcased around 3,000 products, including humanoid robots. China makes the majority of the world's humanoid robots. The U.S. has stepped up scrutiny of robots from the country. Last month, the U.S. Federal Communications Commission announced a ban on imports of new foreign-made humanoid robots. The FCC cited national security reasons in a move that targeted China. The Pentagon recently also added Unitree, one of China's leading humanoid robot makers, to its list of companies that it deemed have ties with the Chinese military. Beijing has hit back at the accusations. At Saturday's opening of the robot games, the organizer and robot makers said that Chinese humanoid robots defeated human world records, as hundreds of humanoid robots marched in formation onto the field in a massive display of synchronized coordination. At a 100-meter sprint, a humanoid robot achieved a result of 9.39 seconds, beating the human record of 9.58 seconds set by Jamaican athlete Bolt in 2009. In a standing high jump, a humanoid robot was able to reach 2.88 meters, well above the 0.95 meters best result by a humanoid in last year's first edition of the games. It surpassed the human high jump record of 2.45 meters set by Cuba's Javier Sotomayor in 1993. Both robots were from Beijing-based X-Humanoid. Before the opening, a humanoid robot from Chinese smartphone company Honor completed a 100-meter sprint in a record of 9.32 seconds during a trial of the games, the company said, at a peak speed of 14.5 meters per second. Still, experts say humanoid robots are still mostly used for demonstrations, performances and research - at least for now - and it will still take time to achieve mass real-world deployment. Some spectators at the robot games said they were excited about the humanoid robots' quickly improving abilities. Humanoid robots are "evolving rapidly," said Li Yanfeng, an education worker and a Beijing resident. "At first, I wasn't very accepting of artificial intelligence. I was even a bit resistant to it, because of the possibility that it might replace or displace humans," she said. "But now that I see this development is unstoppable, I decided to come and take a look." "These sports are perfectly normal for humans, but now robots can do them. I find it amazing," said Yang Shangzheng, another spectator. Liu Tao, who was watching the games with his son, said that he was hoping to see "the best robots China currently has to offer." This year's robot games - which the organizer said has 16 countries participating, among them Germany, Japan and the U.S. - also include other events such as weightlifting and tug of war. (Only the headline and picture of this report may have been reworked by the Business Standard staff; the rest of the content is auto-generated from a syndicated feed.) Don't miss the most important news and views of the day. Get them on our Telegram channel First Published: Aug 23 2026 | 10:43 AM IST
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