Tacser and Action-Conditioned Latent Filter for Generalizable Robotic Surface Perception

Perceiving the physical properties of different surfaces/textures via tactile sensing has been a long-standing problem in robotics. Most prior work has been limited to discriminative models that classify textures into a fixed set of categories. However, to enable seamless and efficient autonomous manipulation, robots must infer physical properties as structured, continuous variables rather than as discrete class labels. In this work, we present a novel deep state-space model (DSSM) to learn and infer key causal textural properties in an unsupervised manner. Using variational inference to solve the DSSM, our proposed Latent Filter allows robotic systems to perceive textures in a continuous and generalizable manner. In addition, we explore a novel interaction approach: Tacser (Tactile Enhancer), to further enhance tactile sensing through vibrations induced by high-frequency micro-movements and thereby improve perception. We evaluated our approach against state-of-the-art techniques and performed extensive ablation studies to demonstrate its effectiveness. This work advances tactile-based texture perception, providing a generalizable and comprehensive framework for robotics.