Robust Adaptive Safe Robotic Grasping with Tactile Sensing

TL;DR

This paper introduces a safety-guaranteed robotic grasping framework using tactile sensing and control barrier functions, validated through simulations and real-world experiments on fragile objects.

Contribution

It develops an integrated approach combining tactile feedback, safety filters, and control barrier functions for safe robotic grasping with formal guarantees.

Findings

Disturbance observer-based safety filters outperform others in safety and conservatism.

Framework successfully grasps fragile objects without damage in real-world tests.

Simulation results confirm effectiveness of safety filters in preventing safety violations.

Abstract

Robotic grasping requires safe force interaction to prevent a grasped object from being damaged or slipping out of the hand. In this vein, this paper proposes an integrated framework for grasping with formal safety guarantees based on Control Barrier Functions. We first design contact force and force closure constraints, which are enforced by a safety filter to accomplish safe grasping with finger force control. For sensory feedback, we develop a technique to estimate contact point, force, and torque from tactile sensors at each finger. We verify the framework with various safety filters in a numerical simulation under a two-finger grasping scenario. We then experimentally validate the framework by grasping multiple objects, including fragile lab glassware, in a real robotic setup, showing that safe grasping can be successfully achieved in the real world. We evaluate the performance of each safety filter in the context of safety violation and conservatism, and find that disturbance observer-based control barrier functions provide superior performance for safety guarantees with minimum conservatism.