Year: 2026
Type: article
Abstract: To enable robots to perform human-like dexterous manipulation, it is essential to understand how mechanical compliance, multi-modal sensing, and purposeful interaction jointly shape tactile perception. In this study, we use a dedicated modular e-Skin with interchangeable mechanical compliance and multi-modal sensing to systematically investigate how sensing embodiment and interaction strategies influence robotic perception of objects. Leveraging a curated set of soft wave objects with controlled viscoelastic and surface properties, we explore a rich set of palpation primitives that vary in indentation depth, frequency, and directionality. In addition, we propose the latent filter , an unsupervised, action-conditioned deep state-space model of the sophisticated interaction dynamics, and infer causal mechanical properties into a structured latent space. This provides in-depth, interpretable representation of how embodiment and interaction determine and influence perception. Our investigation demonstrates that multi-modal sensing outperforms unimodal sensing, emphasizing complex interaction between the environment and the mechanical properties of e-Skin.
Source: npj Robotics
Institutions: Imperial College London, University of Manchester
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,990
Funder: Horizon 2020 Framework Programme
Award: G265104915
OpenAlex ID: W7128625745