Lex Fridman PodcastLeslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15
Episode Details
EPISODE INFO
- Released
- March 12, 2019
- Duration
- 1h 1m
- Channel
- Lex Fridman Podcast
- Watch on YouTube
- ▶ Open ↗
EPISODE DESCRIPTION
Leslie Kaelbling is a roboticist and professor at MIT. She is recognized for her work in reinforcement learning, planning, robot navigation, and several other topics in AI. She won the IJCAI Computers and Thought Award and was the editor-in-chief of the prestigious Journal of Machine Learning Research. Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep15-sb See below for timestamps, and to give feedback, submit questions, contact Lex, etc. *CONTACT LEX:* *Feedback* - give feedback to Lex: https://lexfridman.com/survey *AMA* - submit questions, videos or call-in: https://lexfridman.com/ama *Hiring* - join our team: https://lexfridman.com/hiring *Other* - other ways to get in touch: https://lexfridman.com/contact *PODCAST LINKS:*
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SPEAKERS
Lex Fridman
hostLeslie Kaelbling
guestNarrator
other
EPISODE SUMMARY
In this episode of Lex Fridman Podcast, featuring Lex Fridman and Leslie Kaelbling, Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 explores leslie Kaelbling on uncertainty, abstraction, and truly intelligent robots Leslie Kaelbling discusses her path from philosophy to AI and robotics, arguing that logic, formal semantics, and materialism naturally underpin her view that human-level robot behavior is a purely technical challenge. She traces the cyclical history of AI, contrasting expert systems, symbolic reasoning, and modern learning-based approaches, emphasizing the central role of abstraction, uncertainty, and hierarchical planning. A major theme is planning and acting under partial observability via POMDPs and belief-space planning, and how robots must reason about both the world and their own uncertainty. She also critiques current research culture and publishing, champions open access and deeper theory, and stresses the need to design good objectives and structural biases rather than rely on monolithic end-to-end learning.
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