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

## Title:

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

## Authors:

Ricardo Martins [ website | social ]

Joao Filipe Ferreira [ website | social ]

Jorge Dias [ website | social ]

## 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 than1cm (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.

## Publisher:

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014

14-09-2014

## DOI:

10.1109/IROS.2014.6942711

## Keywords:

robotics, cognitive robotics, touch attention, tactile attention, artificial perception, Bayesian modelling, path planning, haptic exploration, probabilistic grid maps

## Support Materials - Videos:

Detailed representation of the mechanisms involved in the inference of the next workspace region to be explored by the robotic system:

Scenario I - Trial | 01 | 02 | 03 | 04 | 05 | 06 | 07 | 08 | 09 | 10 |

Scenario II - Trial | 01 | 02 | 03 | 04 | 05 | 06 | 07 | 08 | 09 | 10 |

Scenario III - Trial | 01 | 02 | 03 | 04 | 05 | 06 | 07 | 08 | 09 | 10 |

## Thumbnail:

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