Shadow: セグメンテーションマスクを活用した異なるロボット間のポリシー転移
Shadow: Leveraging Segmentation Masks for Cross-Embodiment Policy Transfer
訓練時と評価時にロボットをソースとターゲットの合成セグメンテーションマスクに置き換えることで、単一のロボットアームの専門家軌道から別のロボットアームへポリシーを効率的に転移させる手法を提案。
著者: Marion Lepert, Ria Doshi, Jeannette Bohg
分類: cs.RO
原文アブストラクト
Data collection in robotics is spread across diverse hardware, and this variation will increase as new hardware is developed. Effective use of this growing body of data requires methods capable of learning from diverse robot embodiments. We consider the setting of training a policy using expert trajectories from a single robot arm (the source), and evaluating on a different robot arm for which no data was collected (the target). We present a data editing scheme termed Shadow, in which the robot during training and evaluation is replaced with a composite segmentation mask of the source and target robots. In this way, the input data distribution at train and test time match closely, enabling robust policy transfer to the new unseen robot while being far more data efficient than approaches that require co-training on large amounts of data from diverse embodiments. We demonstrate that an approach as simple as Shadow is effective both in simulation on varying tasks and robots, and on real robot hardware, where Shadow demonstrates an average of over 2x improvement in success rate compared to the strongest baseline.
