High-dynamic Tactile Sensing for Tactile Servo Manipulation: Let Robots Swing a Hammer

Abstract

High-dynamic tactile sensing and tactile servo control present challenges in robustness and real-time performance. This paper proposes a closed-loop tactile servo control strategy for robotic nail hammering, by allowing controlled hammer slide within a rigid robotic 2-finger gripper. The proposed approach detects tactile information of continuous sliding and sliding-induced vibrations in real time and modulates gripping force. The control encourages rotational sliding to enhance impact and reduce recoil while restricting parallel slippage to maintain grip stability. To achieve real-time processing and effective sliding feature extraction, we employ Short-Time Fourier Transform (STFT) and a dual-stream Physics-Informed Machine Learning (PIML) model, processing tactile data at 1 kHz with an average latency of 1.04 ms. Experimental results show that, compared to conventional methods, controlling hammer slippage reduces arm joint recoil by 64.26% (223.30 N → 79.81 N) while increasing hammer impact force by 179.97% (28.06 N → 78.56 N). The method adapts to hammers with varying mass distributions, significantly improving impact resilience and manipulation performance in high-dynamic interactions. These advancements pave the way for more dexterous and robust robotic systems with embodied intelligence.