Ep. 37: Sergey Levine on How Deep Learning Will Unleash a Robotics Revolution
Aug 30, 2017
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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.
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.
The Importance of Generalization
Generalization in robotics is crucial for machines to function effectively in dynamic real-world environments. Instead of simply repeating memorized actions, robots need to adapt their learned experiences to new scenarios they encounter. For instance, a robot trained to swing a golf club must be able to adjust its movements based on different golf courses and conditions. This ability to generalize allows robots to perform a wide array of tasks, enhancing their applicability and usefulness in varied contexts.
Robots Sharing Knowledge
One significant advancement in robotics lies in the potential for robots to share their learning experiences with each other. Unlike human learning, where knowledge transfer can be slow and variable, robots can easily copy and disseminate their learned skills and experiences across multiple units. This allows a large fleet of robots to collectively benefit from each one's experiences, drastically reducing the time needed to reach proficiency. Through this interconnected learning, robots can continually improve their capabilities in real-time, making them increasingly effective in their designated tasks.
The robots that have taken on tasks in the real world - which is to say the world where physics apply - are primarily programmed to do a specific job, such as welding a joint in a car or sweeping up cat hair. So what if robots could learn, and take it a step further - what if they could teach themselves, and pass on their knowledge to other robots? Where could that take machines, and the notion of machine intelligence? And how fast could we get there? Those are the questions our guest Sergey Levine, an assistant professor at UC Berkeley's department of Electrical Engineering and Computer Sciences, is finding answers to.
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