• Classified by Topic • Classified by Publication Type • Sorted by Date • Sorted by First Author Last Name • Classified by Funding Source •
MEReQ: Max-Ent Residual-Q Inverse RL for Sample-Efficient Alignment from Intervention.
Yuxin
Chen, Chen Tang, Jianglan Wei, Chenran Li, Ran
Tian, Xiang Zhang, Wei Zhan, Peter Stone, and Masayoshi Tomizuka.
In
Conference on Robot Learning (CoRL), September 2025.
Aligning robot behavior with human preferences is crucial for deploying embodied AI agents in human-centered environments. A promising solution is interactive imitation learning from human intervention, where a human expert observes the policy's execution and provides interventions as feedback. However, existing methods often fail to utilize the prior policy efficiently to facilitate learning, thus hindering sample efficiency. In this work, we introduce Maximum-Entropy Residual-Q Inverse Reinforcement Learning, designed for sample-efficient alignment from human intervention. Instead of inferring the complete human behavior characteristics, MEReQ infers a residual reward function that captures the discrepancy between the human expert's and the prior policy's underlying reward functions. It then employs Residual Q-Learning (RQL) to align the policy with human preferences using this residual reward function. Extensive evaluations on simulated and real-world tasks demonstrate that MEReQ achieves sample-efficient policy alignment from human intervention compared to other baselines.
@InProceedings{mereq_corl2025,
author = {Yuxin Chen and Chen Tang and Jianglan Wei and Chenran Li and Ran Tian and Xiang Zhang and Wei Zhan and Peter Stone and Masayoshi Tomizuka},
title = "{MEReQ}: Max-Ent Residual-{Q} Inverse {RL} for Sample-Efficient Alignment from Intervention",
booktitle = {Conference on Robot Learning (CoRL)},
year = {2025},
month = {September},
location = {Seoul, Korea},
abstract = {Aligning robot behavior with human preferences is crucial for deploying embodied AI agents in human-centered environments. A promising solution is interactive imitation learning from human intervention, where a human expert observes the policy's execution and provides interventions as feedback. However, existing methods often fail to utilize the prior policy efficiently to facilitate learning, thus hindering sample efficiency. In this work, we introduce Maximum-Entropy Residual-Q Inverse Reinforcement Learning, designed for sample-efficient alignment from human intervention. Instead of inferring the complete human behavior characteristics, MEReQ infers a residual reward function that captures the discrepancy between the human expert's and the prior policy's underlying reward functions. It then employs Residual Q-Learning (RQL) to align the policy with human preferences using this residual reward function. Extensive evaluations on simulated and real-world tasks demonstrate that MEReQ achieves sample-efficient policy alignment from human intervention compared to other baselines.},
}
Generated by bib2html.pl (written by Patrick Riley ) on Wed Jun 10, 2026 15:26:44