Beyond Action Residuals: Real-World Robot Policy Steering via Bottleneck Latent Reinforcement Learning
Overview
This paper introduces ZPerturbation Reinforcement Learning (ZPRL), a method for adapting pretrained robot policies through online reinforcement learning in a compact bottleneck latent space. By applying reward-driven residual perturbations to a frozen pretrained policy, ZPRL achieves improved sample efficiency and higher success rates on real-world manipulation tasks compared to baseline imitation policies, while generating smooth actions without additional regularization.
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“rcentage points over imitation base policies. Our qualitative and quantitative results also indicate that ZPRL combines task success with smooth actions without explicit temporal filtering or smoothness regularization. These results suggest that a compact, task-aligned bottleneck latent provides an effective interface for online RL adaptation. Videos and code can be found at https://manutdmoon.github.io/ZPRL/.”
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- arXiv Robotics — research abstractsSignalBeyond Action Residuals: Real-World Robot Policy Steering via Bottleneck Latent Reinforcement Learning
This paper introduces ZPerturbation Reinforcement Learning (ZPRL), a method for adapting pretrained robot policies through online reinforcement learning in a compact bottleneck latent space. By applying reward-driven residual perturbations to a frozen pretrained policy, ZPRL achieves improved sample efficiency and higher success rates on real-world manipulation tasks compared to baseline imitation policies, while generating smooth actions without additional regularization.
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