ExRL: Sample-Efficient Online Adaptation of Long-Chunk Policies via Execution Length Control
Overview
This research introduces ExRL, a method that shifts adaptation from optimizing actions to optimizing execution length for long-chunk robot policies. By using a lightweight adapter trained with off-policy RL algorithms, ExRL reduces the dimensionality of the adaptation space while maintaining expressivity. The approach enables sample-efficient adaptation across diverse long-chunk policies in both simulation and real-world robotic tasks with minimal task-specific hyperparameter tuning.
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- arXiv Robotics — research abstractsSignalExRL: Sample-Efficient Online Adaptation of Long-Chunk Policies via Execution Length Control
This research introduces ExRL, a method that shifts adaptation from optimizing actions to optimizing execution length for long-chunk robot policies. By using a lightweight adapter trained with off-policy RL algorithms, ExRL reduces the dimensionality of the adaptation space while maintaining expressivity. The approach enables sample-efficient adaptation across diverse long-chunk policies in both simulation and real-world robotic tasks with minimal task-specific hyperparameter tuning.
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