固有感覚・触覚のみで指歩行による把持内操作を学習する可能性
On the Feasibility of Learning Finger-gaiting In-hand Manipulation with Intrinsic Sensing
モデルフリー強化学習を用い、機体搭載の固有感覚と触覚フィードバックだけで精密把持による指歩行を学習し、物体の大角度回転を実現した。初期状態分布の工夫でサンプル効率と頑健性を向上させ、未知物体にも転移可能なことを示した。
著者: Gagan Khandate, Maxmillian Haas-Heger, Matei Ciocarlie
分類: cs.RO, cs.AI
原文アブストラクト
Finger-gaiting manipulation is an important skill to achieve large-angle in-hand re-orientation of objects. However, achieving these gaits with arbitrary orientations of the hand is challenging due to the unstable nature of the task. In this work, we use model-free reinforcement learning (RL) to learn finger-gaiting only via precision grasps and demonstrate finger-gaiting for rotation about an axis purely using on-board proprioceptive and tactile feedback. To tackle the inherent instability of precision grasping, we propose the use of initial state distributions that enable effective exploration of the state space. Our method can learn finger-gaiting with significantly improved sample complexity than the state-of-the-art. The policies we obtain are robust and also transfer to novel objects. Videos can be found at https://roamlab.github.io/learnfg/
