デモンストレーション周辺での適応的探索のためのエルゴード模倣
Ergodic Imitation for Adaptive Exploration around Demonstrations
模倣学習における訓練と実環境のミスマッチに対応するため、デモの幾何学的構造から目標分布を構築し、追従と探索を適応的に切り替えるエルゴード制御に基づく軌道生成手法を提案した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Ziyi Xu, Cem Bilaloglu, Yiming Li, Sylvain Calinon
分類: cs.RO
原文アブストラクト
In robotics, a common challenge in imitation learning is the mismatch between training and deployment conditions, caused, for example, by environmental changes or imperfect observation and control. When a robot follows a nominal trajectory under such mismatch, it may become stuck and fail to complete the task. This calls for adaptive online exploration strategies that remain grounded in demonstrations. To this end, we propose an adaptive ergodic imitation approach that constructs a target distribution from the geometry of the retrieved demonstrations and uses it to generate trajectories that adaptively interpolate between tracking and exploration. Our method extends ergodic control beyond its traditional role in area-coverage and search by incorporating demonstrations into a retrieval-based receding-horizon framework for adaptive imitation.
