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Pieter Abbeel wins ACM Prize in Computing
Pieter Abbeel. Photo courtesy of ACM.
Congratulations to Pieter Abbeel who has been awarded the ACM Prize in Computing for his contribution to robot learning, including learning from demonstrations and deep reinforcement learning for robotic control.
Pieter’s research has covered the following:
- The development of new apprenticeship learning techniques to significantly improve robotic manipulation.
- The introduction of new methods to enhance robot visual perception, physics-based tracking, control, and learning from demonstration
- Development of robots that can perform surgical suturing, detect objects, and plan their trajectories in uncertain situations
- “Few-shot imitation learning,” where a robot is able to learn to perform a task from just one demonstration after having been pre-trained with a large set of demonstrations on related tasks.
- Deep reinforcement learning for robotics.
- The development of a deep reinforcement learning method called Trust Region Policy Optimization. This method stabilizes the reinforcement learning process, enabling robots to learn a range of simulated control skills.
Pieter Abbeel is a Professor of Computer Science and Electrical Engineering at the University of California, Berkeley and the Co-Founder, President and Chief Scientist at Covariant, an AI robotics company. He also hosts the The Robot Brains podcast.
About the ACM Prize in Computing
The ACM Prize in Computing recognizes an early- to mid-career fundamental, innovative contribution in computing that, through its depth, impact and broad implications, exemplifies the greatest achievements in the discipline. The award carries a prize of $250,000.
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