Robust Adaptive Safe Robotic Grasping with Tactile Sensing

2024-11-12

European Control Conference

AI Summary

Safe and reliable grasping of fragile objects remains challenging due to difficulties in regulating contact forces—often leading to slippage or fracture. Method: This paper proposes a haptic-driven adaptive safe grasping framework based on Control Barrier Functions (CBFs). It integrates high-fidelity tactile sensing modeling with inverse estimation to reconstruct real-time contact points, forces, and torques; further, it introduces a disturbance observer–enhanced CBF embedded within a safety filter, explicitly constraining fingertip forces and force-closure conditions. Contribution/Results: The approach guarantees formal safety while significantly reducing conservatism. Evaluated in simulation and on a physical two-finger robotic platform, it demonstrates robust, stable grasping of delicate objects (e.g., glassware), achieving a substantial reduction in safety violations and confirming strong robustness and practical deployability.

Technology Category

• Intelligent Robots: Manipulation
• Natural Language Processing: Safety and Robustness
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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.

Problem

• Developing safe robotic grasping with tactile sensing and force control
• Enforcing contact force constraints using Control Barrier Functions
• Validating safety filters for fragile object handling without damage

Innovation

• Safe grasping using Control Barrier Functions
• Tactile sensors estimate contact force and torque
• Disturbance observer-based CBF ensures minimal conservatism