ACM has named Pieter Abbeel the recipient of the 2021 ACM Prize in Computing for contributions to robot learning, including learning from demonstrations and deep reinforcement learning for robotic control. Abbeel pioneered teaching robots to learn from human demonstrations ("apprenticeship learning") and through their own trial and error ("reinforcement learning"), which have formed the foundation for the next generation of robotics. Abbeel is a professor at the University of California, Berkeley and the co-founder, president and chief scientist at Covariant, an AI robotics company.
Early in his career, Abbeel developed new apprenticeship learning techniques to significantly improve robotic manipulation. As the field matured, researchers were able to program robots to perceive and manipulate rigid objects such as wooden blocks or spoons. However, programming robots to manipulate deformable objects, such as cloth, proved difficult because the way soft materials move when touched is unpredictable. Abbeel introduced new methods to enhance robot visual perception, physics-based tracking, control, and learning from demonstration. By combining these new methods, Abbeel developed a robot that was able to fold clothes such as towels and shirts an improvement over existing technology that was considered an important milestone at the time.
Abbeel's contributions also include developing robots that can perform surgical suturing, detect objects, and plan their trajectories in uncertain situations. More recently, he has pioneered "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.
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