Center-of-Mass-Based Grasp Pose Adaptation Using 3D Range and Force/Torque Sensing

TL;DR

This paper presents a novel CoM-based grasp adaptation method combining 3D perception and force/torque feedback to optimize grasp stability and wrist effort in robotic manipulation.

Contribution

It introduces an iterative approach that uses both exteroceptive and proprioceptive sensing to adapt grasp points for minimizing wrist torque during object lifting.

Findings

Method reduces wrist torque during grasping.

Experimental validation on WALK-MAN robot demonstrates effectiveness.

Improves grasp reliability and safety.

Abstract

Lifting objects, whose mass may produce high wrist torques that exceed the hardware strength limits, could lead to unstable grasps or serious robot damage. This work introduces a new Center-of-Mass (CoM)-based grasp pose adaptation method, for picking up objects using a combination of exteroceptive 3D perception and proprioceptive force/torque sensor feedback. The method works in two iterative stages to provide reliable and wrist torque efficient grasps. Initially, a geometric object CoM is estimated from the input range data. In the first stage, a set of hand-size handle grasps are localized on the object and the closest to its CoM is selected for grasping. In the second stage, the object is lifted using a single arm, while the force and torque readings from the sensor on the wrist are monitored. Based on these readings, a displacement to the new CoM estimation is calculated. The object is released and the process is repeated until the wrist torque effort is minimized. The advantage of our method is the blending of both exteroceptive (3D range) and proprioceptive (force/torque) sensing for finding the grasp location that minimizes the wrist effort, potentially improving the reliability of the grasping and the subsequent manipulation task. We experimentally validate the proposed method by executing a number of tests on a set of objects that include handles, using the humanoid robot WALK-MAN.