EaDex: 低コストデモからのクロスエンボディ器用操作フレームワーク
EaDex: A Cross-Embodiment Dexterous Manipulation Framework from Low-Cost Demonstrations
単一RGB-Dカメラで人間の手の動きを捉え、MANOモデルとモーションリターゲティングで構造化デモデータを生成し、接触報酬に基づく動的デモアニーリングで学習を効率化する、低コストな多エンボディ器用操作学習フレームワークを提案した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
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5. 議論はある？
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著者: Qian Zhao, Xin Tong, Chengdong Wu, Yang Yang, Yingtian Li
分類: cs.RO
原文アブストラクト
Dexterous manipulation learning has long been hindered by the high costs of data and training, as pure reinforcement learning typically requires large-scale interactive exploration and imitation learning depends on high-quality demonstrations that are expensive to collect. To address this problem, we propose EaDex, a multi-embodiment dexterous manipulation learning framework under low-cost demonstration conditions, which enables rapid generation of demonstration data and consequently reduces training time for efficient dexterous manipulation. At the data level, EaDex captures human hand motions using only a single RGB-D camera and constructs structured demonstration data through MANO-based hand modeling, data normalization, and motion retargeting. At the learning level, we introduce a contact-reward-based dynamic demonstration annealing mechanism, which guides early-stage exploration under demonstration and gradually transitions to autonomous optimization with accumulating contact rewards. Using our custom dataset, we evaluate EaDex on three dexterous hands and three articulated object-opening tasks, covering nine cross-embodiment manipulation settings, achieving a 55.3% relative improvement over the baseline without demonstration annealing. These results validate the effectiveness of the proposed low-cost demonstration pipeline and the dynamic demonstration annealing strategy for dexterous manipulation learning.
