How Do You Build a Better Robot? By Understanding People.
Computer scientist Dorsa Sadigh discusses the growing field of human-robot interaction.
Whether it’s autonomous vehicles or assistive technology in healthcare that can do things like help the elderly do core tasks like feeding themselves, some of the most challenging problems in the field of robotics involve how robots interact with humans, with all of our many complexities.
Drawing from fields as varied as cognitive neuroscience, psychology, and behavioral economics, Stanford computer scientist Dorsa Sadigh is exploring how to train robots to better understand humans – and how to give humans the skills to more seamlessly work with robots.
She discusses these challenges with Stanford Engineering’s The Future of Everything host Professor Russ Altman, a Stanford HAI associate director, in this latest episode.
Stanford HAI's mission is to advance AI research, education, policy and practice to improve the human condition. Learn more.
Related News
AI Companions May Worsen Loneliness for Vulnerable Users, Stanford Study Finds
Stanford research finds that users with limited social networks who seek emotional support from AI companions experience lower well-being.
Stanford research finds that users with limited social networks who seek emotional support from AI companions experience lower well-being.
Open-Weight Models Aren’t Enough. We Need Truly Open Source AI Models for Science and Society.
As Chinese AI closes the capability gap, Washington and Silicon Valley debate open-weight models. Stanford HAI's James Landay says it's the right conversation framed the wrong way.
As Chinese AI closes the capability gap, Washington and Silicon Valley debate open-weight models. Stanford HAI's James Landay says it's the right conversation framed the wrong way.
As artificial intelligence moves beyond language into the physical world through "world models," Stanford researchers warn that policymakers face an even steeper governance challenge than with large language models—and the window to get ahead of the technology is closing fast.
As artificial intelligence moves beyond language into the physical world through "world models," Stanford researchers warn that policymakers face an even steeper governance challenge than with large language models—and the window to get ahead of the technology is closing fast.