Efficient Test-Time Adaptation for Robot Policies
Overview
TEMPO enables real-time robot policy adaptation without reward signals, improving performance by up to 80% in simulation and real-world settings using entropy regularization and memory-guided optimization. arXiv:2610.20318v1 (2026-10-15).
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- arXiv Robotics — research abstractsEfficient Test-Time Adaptation For Robot Policies
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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