Touch-Based Object Localisation with Spatially-Aware Belief Entropy Estimation

Abstract

Robust robotic manipulation in the real world requires coping with incomplete or unreliable sensory input. While vision provides rich information, it often fails in the presence of occlusions, clutter, or poor lighting. In such cases, touch offers a robust alternative, enabling object localisation through contact alone. We present a touch-only global localisation method that operates in continuous state space with a particle belief. Sparse contact/no-contact signals are turned into informative likelihoods via a proximity-aware measurement model, and contact-aware resampling mitigates particle starvation. An information-gathering controller selects actions that maximise expected information gain using a non-parametric entropy estimator sensitive to both observation updates and dynamics. On real hardware, the system reliably localises and then grasps from broad, multi-modal initial beliefs with mode separations up to 0.4 m, far beyond the narrow uncertainty ranges assumed in related work. Information-aware localisation-actions speed up belief convergence and boost grasp success; and ablations in simulation confirm the benefits of the measurement and resampling components.