Anca Dragan: Human-Robot Interaction and Reward Engineering | Lex Fridman Podcast #81

ℹ️ EPISODE INFO

Released

March 19, 2020

Duration

1h 38m

Channel

Lex Fridman Podcast

Watch on YouTube

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📝 EPISODE DESCRIPTION

Anca Dragan is a professor at Berkeley, working on human-robot interaction -- algorithms that look beyond the robot's function in isolation, and generate robot behavior that accounts for interaction and coordination with human beings. This episode is presented by Cash App. Download it & use code "LexPodcast": Cash App (App Store): https://apple.co/2sPrUHe Cash App (Google Play): https://bit.ly/2MlvP5w PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ Full episodes playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 Clips playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOeciFP3CBCIEElOJeitOr41 EPISODE LINKS: Anca's Twitter: https://twitter.com/ancadianadragan Anca's Website: https://people.eecs.berkeley.edu/~anca/ OUTLINE: 0:00 - Introduction 2:26 - Interest in robotics 5:32 - Computer science 7:32 - Favorite robot 13:25 - How difficult is human-robot interaction? 32:01 - HRI application domains 34:24 - Optimizing the beliefs of humans 45:59 - Difficulty of driving when humans are involved 1:05:02 - Semi-autonomous driving 1:10:39 - How do we specify good rewards? 1:17:30 - Leaked information from human behavior 1:21:59 - Three laws of robotics 1:26:31 - Book recommendation 1:29:02 - If a doctor gave you 5 years to live... 1:32:48 - Small act of kindness 1:34:31 - Meaning of life CONNECT:

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🎤 SPEAKERS

• Lex Fridman

  host

• Anca Dragan

  guest

• Narrator

  other

🧾 EPISODE SUMMARY

In this episode of Lex Fridman Podcast, featuring Lex Fridman and Anca Dragan, Anca Dragan: Human-Robot Interaction and Reward Engineering | Lex Fridman Podcast #81 explores designing Robots That Understand Human Intent, Limits, and Preferences Lex Fridman and Anca Dragan discuss human-robot interaction with a focus on how robots can model, predict, and adapt to human behavior and preferences. They explore inverse reinforcement learning, rationality assumptions, and how to reinterpret human 'irrationality' as optimal behavior under different beliefs, constraints, or physics models. A major theme is reward design: how hard it is to specify objectives that elicit the right behavior in all situations, and how robots can learn from 'leaked' information in human actions, corrections, environment, and even emergency stops. They also touch on autonomous driving, semi-autonomous systems, ethical and social dimensions of robots, and broader reflections on meaning, mortality, and what it means to build AI that truly serves humans.

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