Contact Provenance: Fusing Triboelectric Tactile Skins, Admittance Signatures, and Vision to Understand “Who Touched What”

Distinguishing contact types during collaboration is critical but underexplored. Kouris et al. (2016, SMC) study contact distinction under admittance control. Li et al. (2023, ICMRE) show triboelectric nanogenerator (TENG) sensors can provide sensitive tactile signals for HRI safety. McKeague et al. (2013, ICRA) address hand–body association in crowded scenes. We propose a contact provenance system that fuses: (i) TENG-based high-bandwidth tactile impulse signatures on the robot surface, (ii) motor/admittance controller residuals (as in Kouris et al.) to characterize interaction dynamics, and (iii) vision-based hand/body association (McKeague et al.) to infer the likely source of contact. The robot then adapts compliance, speed, and signaling based on provenance (e.g., gentle compliance and a brief auditory “yield” if a human hand brushes, firmness if it’s a static obstacle). We target deployment in “exacting” environments (Tian et al., 2025, HRI workshop) where contacts and near-contacts are inevitable. The novelty is the tri-modal fusion for fine-grained contact attribution and policy adaptation, going beyond binary collision detection. By recognizing unexpected human contacts early and classifying them, robots can proactively de-escalate interactions, improving safety and trust. Impact: a practical pathway to robust close-proximity collaboration in real workplaces, public spaces, and crowded labs.

References:

1. Contact distinction in human-robot cooperation with admittance control. Alexandros Kouris, Fotios Dimeas, N. Aspragathos (2016). IEEE International Conference on Systems, Man and Cybernetics.
2. Hand and body association in crowded environments for human-robot interaction. S. McKeague, Jindong Liu, Guang-Zhong Yang (2013). IEEE International Conference on Robotics and Automation.
3. Human-Robot Interaction in Extreme and Challenging Environments. Leimin Tian, Pamela Carreno-Medrano, Manuel Giuliani, Nick Hawes, Raunak Bhattacharyya, Dana Kulic (2025). IEEE/ACM International Conference on Human-Robot Interaction.
4. Human Robot Interaction with Triboelectric Nanogenerator for Tactile Sensing. Chenxing Li, Yansong Wu, Yinlong Liu, Fabian Schreier, Zhenshan Bing, Alois Knoll, Shahram Eivazi (2023). International Conference on Mechatronics and Robotics Engineering.