把持可能性フィールドの学習による把持指向の非把持操作
Grasp-Oriented Non-Prehensile Manipulation via Learning a Graspability Field
物体を特定の姿勢に整えるのではなく、把持成功率を最大化する姿勢へと導く非把持操作手法を提案。学習した把持可能性フィールドを報酬と終了条件に用いることで、シミュレーションと実機で物体を把持可能な状態へ安定して遷移させる。
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: Licheng Zhong, Gim Hee Lee
分類: cs.RO
原文アブストラクト
Non-prehensile manipulation is often used as a preparatory step for robotic grasping, yet existing approaches typically require a predefined target object pose. In practice, however, objects admit multiple graspable configurations and the desired pose is not known in advance. We reformulate non-prehensile manipulation for grasping as optimizing an object centric graspability objective rather than reaching a specific pose. We construct a graspable set from synthesized grasps and define a graspability field that measures how suitable an object configuration is for successful grasp execution. The scalar measure provides a dense learning signal for reinforcement learning and determines when to terminate manipulation. This yields a closed-loop manipulation-to-grasp pipeline driven by a single policy. Experiments in simulation and on a real robot show that the policy reliably reconfigures objects into graspable states and transitions to grasping without external planners or manually specified stopping conditions. The predicted graspability distance correlates with real world grasp success, which indicates that the learned representation captures grasp feasibility of object configurations.
