Grasping by interconnection : can robust and adaptive grasping emerge from minimal object information?

Engineering notes

Key topics: autonomous driving, control. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

This paper investigates whether robust and adaptive dexterous grasping can emerge from minimal object information by modeling grasping as a dynamic interconnection between the robot and a simplified object representation. Instead of relying on precise object models or large-scale data-driven training, objects are approximated using coarse geometric primitives (sphere, cylinder, and box), each associated with a canonical grasp type from human grasp taxonomies. Grasp execution is formulated within the Virtual Model Control (VMC) framework, where virtual springs and dampers mechanically couple a virtual hand to a virtual object, allowing grasp motions to emerge naturally from the coupled dynamics rather than from predefined trajectories. Structured stiffness distributions and adaptive damping profiles promote coordinated, human-like finger closure while mitigating object ejection during transient phases. The approach is implemented on a Shadow Dexterous Hand and evaluated through robustness and generalization experiments under geometric and pose uncertainties, as well as on a diverse set of everyday objects. Results support the hypothesis that reliable dexterous grasping can arise from minimal yet structured object representations combined with dynamic object–robot interconnection.

Links and sources

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