arXiv AI
Synergistic Action and Style Priors for Learning Legged Locomotion
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Pretraining in Actor-Critic Reinforcement Learning for Locomotion
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CLAM: Continuous Latent Action Models for Robot Learning from Unlabeled Demonstrations
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Latent Policy Steering through One-Step Flow Policies
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Residual Reward Models: Leveraging Prior Knowledge for Efficient Preference-based Reinforcement Learning in Robotics
The paper introduces Residual Reward Models (RRM) to enhance preference‑based reinforcement learning (PbRL) in robotics. RRMs decompose the true reward into a prior component—such as a heuristic, language‑generated, or IRL‑derived reward—and a learned residual that is trained with human preferences. Experiments on Meta‑World, DM‑Control, and a physical Franka Panda robot show that RRMs markedly improve sample efficiency and accelerate policy learning compared to standard PbRL methods.