When Life Gives You BC, Make Q-functions: Extracting Q-values from Behavior Cloning for On-Robot Reinforcement Learning

TL;DR

Q2RL is an offline-to-online reinforcement learning method that extracts Q-functions from behavior cloning policies, enabling efficient robot learning and outperforming state-of-the-art methods in manipulation tasks.

Contribution

The paper introduces Q2RL, a novel algorithm combining Q-estimation and Q-gating to improve offline-to-online learning for robotic manipulation.

Findings

Q2RL outperforms SOTA offline-to-online baselines in success rate and convergence time.

Q2RL enables on-robot RL for contact-rich tasks within 1-2 hours.

Q2RL achieves up to 100% success rate and 3.75x improvement over BC.

Abstract

Behavior Cloning (BC) has emerged as a highly effective paradigm for robot learning. However, BC lacks a self-guided mechanism for online improvement after demonstrations have been collected. Existing offline-to-online learning methods often cause policies to replace previously learned good actions due to a distribution mismatch between offline data and online learning. In this work, we propose Q2RL, Q-Estimation and Q-Gating from BC for Reinforcement Learning, an algorithm for efficient offline-to-online learning. Our method consists of two parts: (1) Q-Estimation extracts a Q-function from a BC policy using a few interaction steps with the environment, followed by online RL with (2) Q-Gating, which switches between BC and RL policy actions based on their respective Q-values to collect samples for RL policy training. Across manipulation tasks from D4RL and robomimic benchmarks, Q2RL outperforms SOTA offline-to-online learning baselines on success rate and time to convergence. Q2RL is efficient enough to be applied in an on-robot RL setting, learning robust policies for contact-rich and high precision manipulation tasks such as pipe assembly and kitting, in 1-2 hours of online interaction, achieving success rates of up to 100% and up to 3.75x improvement against the original BC policy. Code and video are available at https://pages.rai-inst.com/q2rl_website/