Exploiting Kinematic Redundancy for Robotic Grasping of Multiple Objects

TL;DR

This paper introduces a human-inspired approach for robotic hands to grasp multiple objects by leveraging kinematic redundancy, using a novel grasp synthesis algorithm, reachability modeling, and an iterative multi-object grasping strategy.

Contribution

It presents a new grasp synthesis method that utilizes arbitrary hand surface regions and models hand reachability to improve multi-object grasping capabilities.

Findings

Successful grasping of multiple objects demonstrated

Generated grasps are replicable on real robotic hands

Kinematic redundancy exploitation improves grasp stability

Abstract

Humans coordinate the abundant degrees of freedom (DoFs) of hands to dexterously perform tasks in everyday life. We imitate human strategies to advance the dexterity of multi-DoF robotic hands. Specifically, we enable a robot hand to grasp multiple objects by exploiting its kinematic redundancy, referring to all its controllable DoFs. We propose a human-like grasp synthesis algorithm to generate grasps using pairwise contacts on arbitrary opposing hand surface regions, no longer limited to fingertips or hand inner surface. To model the available space of the hand for grasp, we construct a reachability map, consisting of reachable spaces of all finger phalanges and the palm. It guides the formulation of a constrained optimization problem, solving for feasible and stable grasps. We formulate an iterative process to empower robotic hands to grasp multiple objects in sequence. Moreover, we propose a kinematic efficiency metric and an associated strategy to facilitate exploiting kinematic redundancy. We validated our approaches by generating grasps of single and multiple objects using various hand surface regions. Such grasps can be successfully replicated on a real robotic hand.