68 citations · 177 across the 11 of their papers we have counts for
9 papers · 1 filter
Policy Architectures for Compositional Generalization in Control
Allan Zhou, Vikash Kumar, Chelsea Finn +1
Many tasks in control, robotics, and planning can be specified using desired goal configurations for various entities in the environment. Learning goal-conditioned policies is a na…
Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention
Abhishek Gupta, Justin Yu, Tony Z. Zhao +5
Reinforcement Learning (RL) algorithms can in principle acquire complex robotic skills by learning from large amounts of data in the real world, collected via trial and error. Howe…
The Ingredients of Real-World Robotic Reinforcement Learning
Henry Zhu, Justin Yu, Abhishek Gupta +5
The success of reinforcement learning for real world robotics has been, in many cases limited to instrumented laboratory scenarios, often requiring arduous human effort and oversig…
A Game Theoretic Framework for Model Based Reinforcement Learning
Aravind Rajeswaran, Igor Mordatch, Vikash Kumar
Model-based reinforcement learning (MBRL) has recently gained immense interest due to its potential for sample efficiency and ability to incorporate off-policy data. However, desig…
Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning
Abhishek Gupta, Vikash Kumar, Corey Lynch +2
We present relay policy learning, a method for imitation and reinforcement learning that can solve multi-stage, long-horizon robotic tasks. This general and universally-applicable,…
Dynamics-Aware Unsupervised Discovery of Skills
Archit Sharma, Shixiang Gu, Sergey Levine +2
Conventionally, model-based reinforcement learning (MBRL) aims to learn a global model for the dynamics of the environment. A good model can potentially enable planning algorithms…