arXiv Robotics — research abstracts· Motasem Alfarra, Pietro Mazzaglia, Markus Peschl, Daniel Dijkman, Christos Louizos·· 12 hours agoEditorial score65
Efficient Test-Time Adaptation For Robot Policies
Efficient Test-Time Adaptation For Robot Policies
Summary
This research introduces TEMPO, a method for adapting robot policies in real-time without reward signals, using entropy regularization and memory-guided optimization. TEMPO improves performance under environmental shifts in both simulation and real-world settings, achieving up to 80% improvement in a single-device setting across multiple robot platforms.
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Source：arXiv Robotics — research abstracts · arxiv.org