机器人通过触觉反馈实时补全物体形状并优化抓取，提升未知物体操作成功率。
ShapeGrasp: Simultaneous Visuo-Haptic Shape Completion and Grasping for Improved Robot Manipulation
- 结合视觉与触觉信息，迭代更新物体3D形状并规划抓取
- 真实场景中抓取成功率84%~91%，优于基线方法
- 首次实现真实抓取后动态更新形状表示，适合复杂物体操作
人类在抓取陌生物体时，会结合初始视觉估计与交互过程中的触觉及本体感觉反馈。我们提出ShapeGrasp，一种机器人实现该机制的方法。该方法采用迭代式抓取-补全流程，将隐式表面的视觉-触觉形状补全（从部分信息生成完整3D形状）与基于物理的抓取规划相耦合。仅需单个RGB-D视图，ShapeGrasp即可推断完整形状（点云或三角网格），通过刚体仿真生成候选抓取方案，并执行最优可行抓取。每次抓取尝试提供额外几何约束——触觉接触面和夹爪占据空间，用于融合更新物体形状。失败后触发姿态重估与重新抓取，使用更新后的形状。我们在两种不同机器人与夹爪上进行了真实世界评估。据我们所知，这是首个在真实抓取后更新形状表示的方法。在所有测试中，对两种夹爪均取得更优结果：三指夹爪抓取成功率达84%，两指夹爪达91%，同时在所有评估指标上提升了3D形状重建质量。
原文摘要 · Abstract (English)
Humans grasp unfamiliar objects by combining an initial visual estimate with tactile and proprioceptive feedback during interaction. We present ShapeGrasp, a robotic implementation of this approach. The proposed method is an iterative grasp-and-complete pipeline that couples implicit surface visuo-haptic shape completion (creation of full 3D shape from partial information) with physics-based grasp planning. From a single RGB-D view, ShapeGrasp infers a complete shape (point cloud or triangular mesh), generates candidate grasps via rigid-body simulation, and executes the best feasible grasp. Each grasp attempt yields additional geometric constraints -- tactile surface contacts and space occupied by the gripper body -- which are fused to update the object shape. Failures trigger pose re-estimation and regrasping using the refined shape. We evaluate ShapeGrasp in the real world using two different robots and grippers. To the best of our knowledge, this is the first approach that updates shape representations following a real-world grasp. We achieved superior results over baselines for both grippers (grasp success rate of 84% with a three-finger gripper and 91% with a two-finger gripper), while improving the 3D shape reconstruction quality in all evaluation metrics used.
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