PartialBiGrasp：从部分视角推断隐藏局部几何以实现双臂抓取

中文摘要 AI 辅助

双臂机器人抓取对于处理无法用单个机械臂可靠操作的大型、重型及几何复杂物体至关重要。这些大型物体通常仅包含由厚度、边缘结构、夹爪间隙等局部几何属性决定的稀疏可抓取区域。现有双臂抓取方法假设可获取物体的完整点云（其固有包含该几何信息），但在实际场景中可能无法实现。本研究提出PartialBiGrasp，一种直接基于部分点云观测运行的双臂抓取生成框架。我们的模型通过卷积占用网络隐式学习几何特征，实现对可抓取性、无碰撞接触区域及物体厚度的局部推理。我们利用该理解生成力闭合兼容的抓取对，进一步通过基于采样的优化进行细化，以修正不完整几何导致的歧义。我们在新物体的含噪部分点云上，采用解析力闭合指标、大规模模拟实验及真实机器人评估对所提方法进行验证，证明其可生成鲁棒且物理稳定的双臂抓取。

英文摘要

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.

发表机构

• Robotics Research Center IIIT Hyderabad（印度信息技术研究所海得拉巴分校机器人研究中心）

机构由 AI 辅助整理，请以论文原文为准。