用触觉反馈实现安全抓取，防止物品损坏或滑落。
Robust Adaptive Safe Robotic Grasping with Tactile Sensing
- 通过安全滤波器控制手指受力，确保抓取时力道可控。
- 实验验证可在真实场景中稳定抓取易碎玻璃器皿。
- 基于扰动观测器的控制屏障函数最安全且保守性最低。
机器人抓取需确保安全的力交互，避免物体损坏或滑脱。本文提出一种基于控制屏障函数（Control Barrier Functions）的集成框架，实现形式化安全保证。首先设计接触力与力闭合约束，由安全滤波器执行以实现手指力控下的安全抓取。在传感反馈方面，开发了从各指触觉传感器估计接触点、力和力矩的技术。在双指抓取场景的数值仿真中验证了多种安全滤波器的有效性。随后在真实机器人平台上实验验证，成功抓取多种物体，包括易碎实验室玻璃器皿。评估各安全滤波器在安全违规与保守性方面的表现，发现基于扰动观测器的控制屏障函数在保证安全的同时最小化保守性，性能最优。
原文摘要 · Abstract (English)
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.
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