PMTRM: Pseudo-Memory Temporal Re-encoding Module for Embodied Policy Learning
Overview
PMTRM enhances task success in phase-ambiguous scenarios by encoding historical states and actions, using temporal heterogeneity and reconstruction losses without altering the action space. arXiv:2610.11168v1 reports that robotic manipulation often involves repeated motions with similar local observations at different phases, supporting the need for temporal re-encoding in policy learning.
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- arXiv Robotics — research abstractsPMTRM: A Lightweight Module for Embodied Policy Learning
PMTRM improves task success in phase-ambiguous scenarios by encoding historical states and actions. It uses temporal heterogeneity and reconstruction losses to enhance policy learning without altering the original action space.
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