{"id":1057371,"date":"2025-01-17T15:19:21","date_gmt":"2025-01-17T23:19:21","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/"},"modified":"2026-01-21T17:39:05","modified_gmt":"2026-01-22T01:39:05","slug":"physical-ai-research","status":"publish","type":"msr-group","link":"https:\/\/www.microsoft.com\/en-us\/research\/collaboration\/physical-ai-research\/","title":{"rendered":"Physical AI research"},"content":{"rendered":"
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Physical AI research<\/h1>\n\n\n\n

Developing intelligent systems that perform complex tasks through understanding and engaging with physical environments<\/p>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n<\/section>\n\n\n\n\n\n

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What is physical AI?<\/h2>\n\n\n\n

Physical AI systems interact with and learn from the physical world through sensory inputs and actions, using robotic tools to perceive, navigate, and interact with their environment.<\/p>\n\n\n\n

Our mission is to accelerate and advance research to develop AI agents that can:\u202f<\/p>\n\n\n\n

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  1. Learn by interacting with the real world<\/li>\n\n\n\n
  2. Adapt to dynamic environments through trial and error<\/li>\n\n\n\n
  3. Transfer knowledge in physical spaces<\/li>\n\n\n\n
  4. Translate perceptions into actions for completing everyday tasks like opening doors, picking up objects, or navigating around obstacles<\/li>\n<\/ol>\n<\/div>