• DOI:10.1109/IROS.2014.6942711
• Corpus ID: 8972722

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

@article{Martins2014TouchAB,
  title={Touch attention Bayesian models for robotic active haptic exploration of heterogeneous surfaces},
  author={Ricardo Martins and Jo{\~a}o Filipe Ferreira and J. Dias},
  journal={2014 IEEE/RSJ International Conference on Intelligent Robots and Systems},
  year={2014},
  pages={1208-1215}
}

• Ricardo Martins, J. Ferreira, J. Dias
• Published 22 September 2014
• Computer Science
• 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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 πper perceives and discriminates different categories of materials (haptic stimulus) integrating compliance and texture features extracted from haptic sensory data. The model πtar 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 πper 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.