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.
Dutta et al. (2026) studied this question.