Tactile-Proprioceptive Sensor Fusion for Contact Wrench Estimation in Whole-Body Physical Human-Robot Interaction

2026-05-27

AI Summary

This work addresses the challenge of accurate contact torque estimation in whole-body physical human–robot interaction, where performance is often degraded by friction disturbances and ambiguities in sensor data. The authors propose a multimodal approach that fuses pneumatic tactile skin with motor current-based proprioception to implicitly disentangle external contact forces from residual friction effects. By leveraging tactile cues and employing a temporal convolutional network (TCN) to model the hysteresis inherent in stick–slip transitions, the method achieves high-fidelity, smooth multi-axis contact force reconstruction from initial contact without requiring explicit friction identification. Experimental validation on a tactile-skin-integrated robotic arm demonstrates substantial improvements over unimodal baselines, exhibiting enhanced sensitivity and responsiveness under both static and dynamic contact conditions, while simultaneously enabling reliable force estimation and kinesthetic teaching.

Abstract

Direct physical guidance is a natural means of teaching and interacting with robots, and robotic skins make a key contribution by enabling sensitive contact sensing and localization. This paper presents a tactile-proprioceptive sensor fusion framework for natural physical human-robot interaction. Tactile cues from pneumatic skin pads serve as contact indicators that bypass the ambiguity between frictional residues and applied external forces, enabling highly sensitive contact detection without explicit friction identification. We fuse these cues with motor-current-based proprioception to reconstruct multi-axis contact forces on the robot surface. To maintain accuracy during motion, we employ a temporal convolutional network (TCN) to mitigate friction hysteresis during stick-slip transitions, reducing uncertainty at contact onset and yielding smooth, responsive guidance. We validate the approach on a skin-integrated robot arm: (i) multi-axis forces are reconstructed in stationary contacts, and (ii) simultaneous force estimation and kinesthetic teaching are demonstrated. Results indicate improved sensitivity and responsiveness across diverse contact conditions compared with tactile-only and proprioceptive-only baselines, supporting tactile-proprioceptive fusion as a reliable pathway to safe, intuitive physical human-robot interaction.

Problem

• contact wrench estimation
• tactile-proprioceptive fusion
• physical human-robot interaction
• friction hysteresis
• contact detection

Innovation

• tactile-proprioceptive fusion
• contact wrench estimation
• pneumatic skin
• temporal convolutional network
• physical human-robot interaction