Optimization Model for Planning Precision Grasps with Multi-Fingered Hands

TL;DR

This paper introduces an optimization model that efficiently finds collision-free precision grasps for multi-fingered hands using noisy point cloud data, enhancing robustness and accuracy in complex object manipulation.

Contribution

It proposes a novel optimization approach that accounts for collision and noise, enabling effective grasp planning on complex objects from point cloud data.

Findings

Average grasp computation time is 0.50 seconds.

Robustness to noise and incomplete data demonstrated.

Effective on complex-shaped objects.

Abstract

Precision grasps with multi-fingered hands are important for precise placement and in-hand manipulation tasks. Searching precision grasps on the object represented by point cloud, is challenging due to the complex object shape, high-dimensionality, collision and undesired properties of the sensing and positioning. This paper proposes an optimization model to search for precision grasps with multi-fingered hands. The model takes noisy point cloud of the object as input and optimizes the grasp quality by iteratively searching for the palm pose and finger joints positions. The collision between the hand and the object is approximated and penalized by a series of least-squares. The collision approximation is able to handle the point cloud representation of the objects with complex shapes. The proposed optimization model is able to locate collision-free optimal precision grasps efficiently. The average computation time is 0.50 sec/grasp. The searching is robust to the incompleteness and noise of the point cloud. The effectiveness of the algorithm is demonstrated by experiments.