Object recognition using tactile feedback is another problem that may be approached using active information acquisition. Our work focuses on adaptive selection of sequences of wrist poses and enclosure grasps in order to achieves accurate touch-only recognition. We formulate an optimal control problem to minimize the number of touches and the probability of an incorrect object classification. The classic separation principle does not hold for such active classification and hypothesis testing problems because the discrete measurements and states (object classes) violate the necessary linear Gaussian assumptions. To enable efficient, yet non-greedy closed-loop planning, our work develops Monte Carlo tree search algorithms that approximate the optimal sequence of wrist poses.
@inproceedings{Zhang_ActiveTouch_IROS17,
author = {M. Zhang and N. Atanasov and K. Daniilidis},
title = {Active End-Effector Pose Selection for Tactile Object Recognition through Monte Carlo Tree Search},
booktitle = {IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
year = {2017},
doi = {http://www.doi.org/10.1109/IROS.2017.8206161}
}
@inproceedings{Lauri_MonteCarloTreeSearch_ICRA15_Workshop,
author = {M. Lauri and N. Atanasov and G. Pappas and R. Ritala},
title = {Active Object Recognition via Monte Carlo Tree Search},
booktitle = {Workshop on Beyond Geometric Constraints at ICRA},
year = {2015}
}