PMTRM: 疑似記憶時系列再符号化モジュールによる身体性ポリシー学習
PMTRM: Pseudo-Memory Temporal Re-encoding Module for Embodied Policy Learning
局所観測が似ているが異なる行動が必要な位相の曖昧さに対処するため、実行済み状態と行動の履歴を潜在系列に符号化する軽量プラグインモジュールを提案し、シミュレーションと実機でタスク成功率を向上させた。
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著者: Changchuan Yang, Haoxuan Xu, Wenbo Chen, Shuai Ren, Jianlong Zheng, Huarui Zhang, Tianfu Li, Guanzhong Tian
分類: cs.RO, cs.AI
原文アブストラクト
Robotic manipulation often contains repeated motions whose local observations look similar at different phases. When these phases require different actions, a policy that relies mainly on the current observation may repeat completed motions or switch phases at the wrong time. To address this phase ambiguity, we present the Pseudo-Memory Temporal Re-encoding Module (PMTRM), a lightweight plug-in module with only 7.61M parameters that encodes a bounded history of executed states and actions into a latent sequence for existing policies. To help distinguish phases, a temporal heterogeneity objective penalizes positive similarity between distant positions in this sequence, while anchor and reconstruction losses preserve information needed for action prediction. The reconstruction decoder is used only during training, leaving the temporal re-encoder to supply history to the policy at inference. We train the module progressively on synthetic sequences and robot data, then jointly with the policy, using temporal masking to accommodate partial histories. This integration retains the original action head and action space and adds auxiliary losses to the original policy loss. Experiments with multiple policy backbones in simulation and on a real robot show improved task success on tasks with phase ambiguity, with little additional computation.