部分観測からの隠れた局所形状推定による双腕把持: PartialBiGrasp
PartialBiGrasp: Inferring Hidden Local Geometry for Bimanual Grasping from Partial Views
部分点群のみから双腕ロボットの把持ペアを生成するフレームワークを提案。畳み込み占有ネットワークで局所形状を学習し、力学的閉鎖を満たす把持をサンプリング最適化で洗練する。
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著者: Ayush Kaura, Vignesh Vembar, Md Faizal Karim, Keshab Patra, K Madhava Krishna
分類: cs.RO
原文アブストラクト
Dual-arm robotic grasping is essential for manipulating large, heavy, and geometrically complex objects that cannot be reliably handled using a single manipulator. These large objects often contain only sparse graspable regions determined by local geometric properties such as thickness, edge structure, and gripper clearance. Prior bimanual grasping methods assume access to a full point cloud of the object which inherently contains this geometric information, but may not be accessible in real scenarios. This work proposes PartialBiGrasp, a dual-arm grasp generation framework that operates directly on partial point cloud observations. Our model learns geometric features implicitly through convolutional occupancy networks, enabling local reasoning about graspability, collision-free contact regions, and object thickness. We leverage this understanding to generate force-closure compliant grasp pairs, which are further refined using a sampling-based optimization to correct for ambiguity caused by incomplete geometry. We evaluate our approach using analytical force-closure metrics, large-scale simulation experiments, and real-world robot evaluations on noisy partial point clouds of novel objects, demonstrating robust and physically stable dual-arm grasp generation.