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The AI Podcast

Ep. 37: Sergey Levine on How Deep Learning Will Unleash a Robotics Revolution

Aug 30, 2017
Sergey Levine, an assistant professor at UC Berkeley, dives into the fascinating world of autonomous learning in robots. He discusses how robots can evolve from performing specific tasks to teaching themselves and each other. The conversation covers the complexities of reinforcement learning, comparing robot adaptability to human learning. Sergey also envisions a future where robots enhance human life, assist the disabled, and tackle hazardous jobs. With transformative potential on the horizon, he highlights both the challenges and the exciting possibilities in robotics.
24:08

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Quick takeaways

  • Robots equipped with reward functions can learn through trial and error, mirroring human learning for autonomy and adaptation.
  • The ability for robots to share learned experiences with each other accelerates proficiency and significantly enhances their performance across tasks.

Deep dives

Robots Learning Through Experience

Teaching robots to learn and adapt involves a different approach than traditional image recognition systems. While deep learning systems typically rely on large datasets of labeled images, robots need to learn by trial and error, similar to human learning. To achieve this, robots are equipped with a reward function that communicates what success looks like, allowing them to understand desirable outcomes. This method enables robots to observe human actions and determine the underlying goals, thereby facilitating autonomous learning through imitation and experience.

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