モンテカルロ木探索による触覚物体認識のための能動的エンドエフェクタ姿勢選択
Active End-Effector Pose Selection for Tactile Object Recognition through Monte Carlo Tree Search
触覚のみで物体を認識するため、把持ごとの手首姿勢をモンテカルロ木探索で適応的に選び、少ない接触回数で認識精度を高める手法を提案し、実機で有効性を示した。
著者: Mabel M. Zhang, Nikolay Atanasov, Kostas Daniilidis
分類: cs.RO
原文アブストラクト
This paper considers the problem of active object recognition using touch only. The focus is on adaptively selecting a sequence of wrist poses that achieves accurate recognition by enclosure grasps. It seeks to minimize the number of touches and maximize recognition confidence. The actions are formulated as wrist poses relative to each other, making the algorithm independent of absolute workspace coordinates. The optimal sequence is approximated by Monte Carlo tree search. We demonstrate results in a physics engine and on a real robot. In the physics engine, most object instances were recognized in at most 16 grasps. On a real robot, our method recognized objects in 2--9 grasps and outperformed a greedy baseline.
