Touch attention Bayesian models for robotic active haptic exploration of heterogeneous surfaces

TL;DR

This paper presents Bayesian touch attention models enabling robots to actively explore and discriminate heterogeneous surface materials with high accuracy, managing uncertainty and guiding exploration effectively.

Contribution

It introduces a novel Bayesian framework for active haptic exploration that discriminates materials and guides exploration based on uncertainty and saliency.

Findings

Discriminates 10 material classes with over 90% accuracy

Successfully follows surface discontinuities within 1cm in simulation

Demonstrates robustness to sensory noise and surface uncertainty

Abstract

This work contributes to the development of active haptic exploration strategies of surfaces using robotic hands in environments with an unknown structure. The architecture of the proposed approach consists two main Bayesian models, implementing the touch attention mechanisms of the system. The model pi_per perceives and discriminates different categories of materials (haptic stimulus) integrating compliance and texture features extracted from haptic sensory data. The model pi_tar actively infers the next region of the workspace that should be explored by the robotic system, integrating the task information, the permanently updated saliency and uncertainty maps extracted from the perceived haptic stimulus map, as well as, inhibition-of-return mechanisms. The experimental results demonstrate that the Bayesian model pi_per can be used to discriminate 10 different classes of materials with an average recognition rate higher than 90% . The generalization capability of the proposed models was demonstrated experimentally. The ATLAS robot, in the simulation, was able to perform the following of a discontinuity between two regions made of different materials with a divergence smaller than 1cm (30 trials). The tests were performed in scenarios with 3 different configurations of the discontinuity. The Bayesian models have demonstrated the capability to manage the uncertainty about the structure of the surfaces and sensory noise to make correct motor decisions from haptic percepts.