MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale

no code implementations • 16 Apr 2021 • Dmitry Kalashnikov, Jacob Varley, Yevgen Chebotar, Benjamin Swanson, Rico Jonschkowski, Chelsea Finn, Sergey Levine, Karol Hausman

In this paper, we study how a large-scale collective robotic learning system can acquire a repertoire of behaviors simultaneously, sharing exploration, experience, and representations across tasks.

reinforcement-learning Reinforcement Learning (RL)

Efficient Adaptation for End-to-End Vision-Based Robotic Manipulation

no code implementations • ICML Workshop LifelongML 2020 • Ryan Julian, Benjamin Swanson, Gaurav S. Sukhatme, Sergey Levine, Chelsea Finn, Karol Hausman

One of the great promises of robot learning systems is that they will be able to learn from their mistakes and continuously adapt to ever-changing environments, but most robot learning systems today are deployed as fixed policies which do not adapt after deployment.

Continual Learning Robotic Grasping

Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning

no code implementations • 21 Apr 2020 • Ryan Julian, Benjamin Swanson, Gaurav S. Sukhatme, Sergey Levine, Chelsea Finn, Karol Hausman

One of the great promises of robot learning systems is that they will be able to learn from their mistakes and continuously adapt to ever-changing environments.

Continual Learning reinforcement-learning +2

Native Language Detection with Tree Substitution Grammars

no code implementations • ACL 2012 • Benjamin Swanson, Eugene Charniak