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A realistic path to robotic foundation models
Jun 5, 2024
Sergey Levine and Chelsea Finn from Physical Intelligence discuss a realistic path to robotic foundation models, key factors for the future of robotics, and the transformerification of robotics. They explore the shift towards horizontal robotics companies and the importance of building general robotics models for various tasks.
07:49
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Quick takeaways
- Robotic learning requires real-world data and evaluation, different from language models.
- Reducing the cost of robots through innovations can drive the success of robotic learning.
Deep dives
Challenges in Robotic Learning Progress
Robotic learning faces unique challenges compared to training language or visual models. Unlike language models, robotic systems require real-world data and evaluation. Multi-robot policies and robotic instruction prompting in plain text are emerging trends that could revolutionize autonomous systems. Building a successful robotic foundation model company entails deploying robots in the real world or funding sufficient robotics work for data gathering.
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Discover
highlights
Listen to the best highlights from the podcasts you love and dive into the full episode