{"id":3955,"date":"2025-04-08T16:41:25","date_gmt":"2025-04-08T16:41:25","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2025\/04\/08\/national-robotics-week-latest-physical-ai-research-breakthroughs-and-resources\/"},"modified":"2025-04-08T16:41:25","modified_gmt":"2025-04-08T16:41:25","slug":"national-robotics-week-latest-physical-ai-research-breakthroughs-and-resources","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2025\/04\/08\/national-robotics-week-latest-physical-ai-research-breakthroughs-and-resources\/","title":{"rendered":"National Robotics Week \u2014 Latest Physical AI Research, Breakthroughs and Resources"},"content":{"rendered":"<div>\n\t\t<span class=\"bsf-rt-reading-time\"><span class=\"bsf-rt-display-label\"><\/span> <span class=\"bsf-rt-display-time\"><\/span> <span class=\"bsf-rt-display-postfix\"><\/span><\/span><\/p>\n<p><em>Check back here throughout the week to learn the latest on <a target=\"_blank\" href=\"https:\/\/www.nvidia.com\/en-us\/glossary\/generative-physical-ai\/?ncid=pa-srch-goog-359637&amp;_bt=736250757250&amp;_bk=nvidia%20physical%20ai&amp;_bm=b&amp;_bn=g&amp;_bg=176691904220&amp;gad_source=1&amp;gclid=EAIaIQobChMIztiCg4-4jAMVgiBECB0y7TSaEAAYASAAEgI0tvD_BwE\" rel=\"noopener\">physical AI<\/a>, which enables machines to perceive, plan and act with greater autonomy and intelligence in real-world environments.<\/em><\/p>\n<p>This National Robotics Week, running through April 12, NVIDIA is highlighting the pioneering technologies that are shaping the future of intelligent machines and driving progress across manufacturing, healthcare, logistics and more.<\/p>\n<p>Advancements in <a target=\"_blank\" href=\"https:\/\/www.nvidia.com\/en-us\/use-cases\/robotics-simulation\/\" rel=\"noopener\">robotics simulation<\/a> and <a target=\"_blank\" href=\"https:\/\/www.nvidia.com\/en-us\/use-cases\/robot-learning\/\" rel=\"noopener\">robot learning<\/a> are driving this fundamental shift in the industry. Plus, the emergence of <a target=\"_blank\" href=\"https:\/\/www.nvidia.com\/en-us\/glossary\/world-models\/?ncid=pa-srch-goog-617078&amp;_bt=738313126223&amp;_bk=world%20foundation%20model&amp;_bm=b&amp;_bn=g&amp;_bg=180166076110&amp;gad_source=1&amp;gclid=EAIaIQobChMIt8mxo4-4jAMV1xFECB14lSGYEAAYASAAEgISsfD_BwE\" rel=\"noopener\">world foundation models<\/a> is accelerating the evolution of AI-enabled robots capable of adapting to dynamic and complex scenarios.<\/p>\n<p>For example, by providing robot foundation models like <a target=\"_blank\" href=\"https:\/\/nvidianews.nvidia.com\/news\/nvidia-isaac-gr00t-n1-open-humanoid-robot-foundation-model-simulation-frameworks\" rel=\"noopener\">NVIDIA GR00T N1<\/a>, frameworks such as <a target=\"_blank\" href=\"https:\/\/developer.nvidia.com\/isaac\/sim\" rel=\"noopener\">NVIDIA Isaac Sim<\/a> and <a target=\"_blank\" href=\"https:\/\/developer.nvidia.com\/isaac\/lab\" rel=\"noopener\">Isaac Lab<\/a> for robot simulation and training, and <a target=\"_blank\" href=\"https:\/\/www.nvidia.com\/en-us\/use-cases\/synthetic-data\/\" rel=\"noopener\">synthetic data generation<\/a> pipelines to help train robots for diverse tasks, the <a target=\"_blank\" href=\"https:\/\/developer.nvidia.com\/isaac\" rel=\"noopener\">NVIDIA Isaac<\/a> and <a target=\"_blank\" href=\"https:\/\/developer.nvidia.com\/isaac\/gr00t\" rel=\"noopener\">GR00T<\/a> platforms are empowering researchers and developers to push the boundaries of robotics.<\/p>\n<p>The <a target=\"_blank\" href=\"https:\/\/lu.ma\/vnndgrnf\" rel=\"noopener\">Seeed Studio Embodied AI Hackathon<\/a>, which took place last month, brought together the robotics community to showcase innovative projects using the <a target=\"_blank\" href=\"https:\/\/www.seeedstudio.com\/SO-ARM100-Low-Cost-AI-Arm-Kit-Pro-p-6343.html\" rel=\"noopener\">LeRobot SO-100ARM<\/a> motor kit.<\/p>\n<p>The event highlighted how <a target=\"_blank\" href=\"https:\/\/www.nvidia.com\/en-us\/use-cases\/robot-learning\/\" rel=\"noopener\">robot learning<\/a> is advancing AI-driven robotics, with teams successfully integrating the <a target=\"_blank\" href=\"https:\/\/nvidianews.nvidia.com\/news\/nvidia-isaac-gr00t-n1-open-humanoid-robot-foundation-model-simulation-frameworks\" rel=\"noopener\">NVIDIA Isaac GR00T N1 model <\/a>to speed humanoid robot development. A notable project involved developing leader-follower robot pairs capable of learning pick-and-place tasks by post-training robot foundation models on real-world demonstration data.<\/p>\n<\/p>\n<p>How the project worked:<\/p>\n<ul>\n<li><b>Real-World Imitation Learning<\/b>: Robots observe and mimic human-led demonstrations, recorded through Arducam vision systems and an external camera.<\/li>\n<li><b>Post-Training Pipeline<\/b>: Captured data is structured into a modality.json dataset for efficient GPU-based training with GR00T N1.<\/li>\n<li><b>Bimanual Manipulation<\/b>: The model is optimized for controlling two robotic arms simultaneously, enhancing cooperative skills.<\/li>\n<\/ul>\n<p>The dataset is now publicly available on <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/jchun\/so100_pickplace_small_20250322_163234\" rel=\"noopener\">Hugging Face<\/a>, with implementation details on <a target=\"_blank\" href=\"https:\/\/github.com\/JChunX\/apple_pie\/tree\/seeed_hacks\" rel=\"noopener\">GitHub<\/a>.<\/p>\n<figure id=\"attachment_79585\" aria-describedby=\"caption-attachment-79585\" class=\"wp-caption alignnone\"><img decoding=\"async\" loading=\"lazy\" class=\"size-full wp-image-79585\" src=\"https:\/\/blogs.nvidia.com\/wp-content\/uploads\/2025\/04\/firebreathingrubberduckies.jpg\" alt=\"\" width=\"800\" height=\"600\"><figcaption id=\"caption-attachment-79585\" class=\"wp-caption-text\">Team \u201cFirebreathing Rubber Duckies\u201d celebrating with NVIDIA hosts.<\/figcaption><\/figure>\n<p>Learn more about the <a target=\"_blank\" href=\"https:\/\/www.hackster.io\/firebreathing-rubber-duckies\/running-lerobot-so-100-arm-on-nvidia-isaac-gr00t-n1-458189\" rel=\"noopener\">project<\/a>.<\/p>\n<p>The IEEE Robotics and Automation Society in March announced the recipients of its <a target=\"_blank\" href=\"https:\/\/www.ieee-ras.org\/about-ras\/latest-news\/2025-ieee-robotics-and-automation-awards-announced\" rel=\"noopener\">2025 Early Academic Career Award<\/a>, recognizing outstanding contributions to the fields of robotics and automation.<\/p>\n<p>This year\u2019s honorees \u2014 including NVIDIA\u2019s Shuran Song, Abhishek Gupta and Yuke Zhu \u2014 are pioneering advancements in scalable robot learning, real-world reinforcement learning and embodied AI. Their work is shaping the next generation of intelligent systems, driving innovation that impacts both research and real-world applications.<\/p>\n<p>Learn more about the award winners:<\/p>\n<ul>\n<li><a target=\"_blank\" href=\"https:\/\/scholar.google.com\/citations?hl=en&amp;user=5031vK4AAAAJ&amp;view_op=list_works&amp;sortby=pubdate\" rel=\"noopener\"><b>Shuran Song<\/b><\/a>, principal research scientist at NVIDIA, was recognized for her contributions to scalable robot learning. Notable recent papers include:\n<\/li>\n<\/ul>\n<ul>\n<li><a target=\"_blank\" href=\"https:\/\/scholar.google.com\/citations?hl=en&amp;user=1wLVDP4AAAAJ\" rel=\"noopener\"><b>Abhishek Gupta<\/b><\/a>, visiting professor at NVIDIA, was honored for his pioneering work in real-world robotic reinforcement learning. Notable recent papers include:\n<\/li>\n<\/ul>\n<ul>\n<li><a target=\"_blank\" href=\"https:\/\/scholar.google.com\/citations?user=mWGyYMsAAAAJ&amp;hl=en\" rel=\"noopener\"><b>Yuke Zhu<\/b><\/a>, principal research scientist at NVIDIA, was awarded for his contributions to embodied AI and widely used open-source software platforms. Notable recent papers include:\n<\/li>\n<\/ul>\n<p>These researchers will be recognized at the <a target=\"_blank\" href=\"https:\/\/2025.ieee-icra.org\/\" rel=\"noopener\">International Conference on Robotics and Automation<\/a> in May.<\/p>\n<p><i>Stay up to date on NVIDIA\u2019s leading robotics research through the Robotics Research and Development Digest (R<\/i><i>2<\/i><i>D<\/i><i>2<\/i><i>) <\/i><a target=\"_blank\" href=\"https:\/\/developer.nvidia.com\/blog\/tag\/robotics-research-development-digest-r2d2\/\" rel=\"noopener\"><i>tech blog series<\/i><\/a><i>, subscribing to this <\/i><a target=\"_blank\" href=\"https:\/\/www.nvidia.com\/en-us\/industries\/robotics\/robotics-stay-informed\/\" rel=\"noopener\"><i>newsletter<\/i><\/a><i> and following NVIDIA Robotics on <\/i><a target=\"_blank\" href=\"https:\/\/www.youtube.com\/channel\/UCSKUoczbGAcMld7HjpCR8OA\" rel=\"noopener\"><i>YouTube<\/i><\/a><i>, <\/i><a target=\"_blank\" href=\"https:\/\/discord.gg\/w9VvuYdq\" rel=\"noopener\"><i>Discord<\/i><\/a> <i>and <\/i><a target=\"_blank\" href=\"https:\/\/forums.developer.nvidia.com\/c\/omniverse\/simulation\/69\" rel=\"noopener\"><i>developer forums<\/i><\/a><i>.<\/i><\/p>\n<\/p><\/div>\n","protected":false},"excerpt":{"rendered":"<p>https:\/\/blogs.nvidia.com\/blog\/national-robotics-week-2025\/<\/p>\n","protected":false},"author":0,"featured_media":3956,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[3],"tags":[],"_links":{"self":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts\/3955"}],"collection":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/comments?post=3955"}],"version-history":[{"count":0,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts\/3955\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/media\/3956"}],"wp:attachment":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/media?parent=3955"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/categories?post=3955"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/tags?post=3955"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}