PartialBiGrasp: Inferring Hidden Local Geometry for Bimanual Grasping from Partial Views
2026-08-19 • Robotics
Robotics
AI summaryⓘ
The authors developed a method called PartialBiGrasp to help robots pick up big and tricky objects using two arms. Unlike earlier methods that need a complete 3D scan of the object, their method works even when only partial views are available, which is more common in real life. They use a special network to understand the object's shape and find good places to grab so the robot won’t drop or collide with the object. They tested their system in simulations and with real robots, showing it can reliably hold objects that are partially seen and noisy.
dual-arm graspingpartial point cloudconvolutional occupancy networksforce-closurecollision-free contactsampling-based optimizationrobotic manipulation3D geometrygrasp generationpoint cloud
Authors
Ayush Kaura, Vignesh Vembar, Md Faizal Karim, Keshab Patra, K Madhava Krishna
Abstract
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.