Residual Off-Policy RL for Finetuning Behavior Cloning Policies
Recent advances in behavior cloning (BC) have enabled impressive visuomotor
control policies. However, these approaches are limited by the quality of human
demonstrations, the manual effort required for data collection, and the
diminishing returns from increasing offline data. In comparison, reinforcement
learning (RL) trains an agent through autonomous interaction with the
environment and has shown remarkable success in various domains. Still,
training RL policies directly on real-world robots remains challenging due to
sample inefficiency, safety concerns, and the difficulty of learning from
sparse rewards for long-horizon tasks, especially for high-degree-of-freedom
(DoF) systems. We present a recipe that combines the benefits of BC and RL
through a residual learning framework. Our approach leverages BC policies as
black-box bases and learns lightweight per-step residual corrections via
sample-efficient off-policy RL. We demonstrate that our method requires only
sparse binary reward signals and can effectively improve manipulation policies
on high-degree-of-freedom (DoF) systems in both simulation and the real world.
In particular, we demonstrate, to the best of our knowledge, the first
successful real-world RL training on a humanoid robot with dexterous hands. Our
results demonstrate state-of-the-art performance in various vision-based tasks,
pointing towards a practical pathway for deploying RL in the real world.
Project website: https://residual-offpolicy-rl.github.io
68d4d93936950a9dff156924
Lars Ankile
68d4d93936950a9dff156925
Zhenyu Jiang
68d4d93936950a9dff156926
Rocky Duan
68d4d93936950a9dff156927
Guanya Shi
68d4d93936950a9dff156928
Pieter Abbeel
68d4d93936950a9dff156929
Anusha Nagabandi
Residual Off-Policy RL for Finetuning Behavior Cloning Policies
Recent advances in behavior cloning (BC) have enabled impressive visuomotor
control policies. However, these approaches are limited by the quality of human
demonstrations, the manual effort required for data collection, and the
diminishing returns from increasing offline data. In comparison, reinforcement
learning (RL) trains an agent through autonomous interaction with the
environment and has shown remarkable success in various domains. Still,
training RL policies directly on real-world robots remains challenging due to
sample inefficiency, safety concerns, and the difficulty of learning from
sparse rewards for long-horizon tasks, especially for high-degree-of-freedom
(DoF) systems. We present a recipe that combines the benefits of BC and RL
through a residual learning framework. Our approach leverages BC policies as
black-box bases and learns lightweight per-step residual corrections via
sample-efficient off-policy RL. We demonstrate that our method requires only
sparse binary reward signals and can effectively improve manipulation policies
on high-degree-of-freedom (DoF) systems in both simulation and the real world.
In particular, we demonstrate, to the best of our knowledge, the first
successful real-world RL training on a humanoid robot with dexterous hands. Our
results demonstrate state-of-the-art performance in various vision-based tasks,
pointing towards a practical pathway for deploying RL in the real world.
Project website: https://residual-offpolicy-rl.github.io
A residual learning framework combines behavior cloning and reinforcement learning to improve manipulation policies on high-degree-of-freedom systems using sparse binary rewards.
behavior cloning reinforcement learning residual learning sample-efficient off-policy RL sparse binary reward signals manipulation policies high-degree-of-freedom systems humanoid robot dexterous hands vision-based tasks