IEEE Transactions on Haptics

IEEE Transactions on Haptics (ToH) has moved to the OnlinePlus publication model starting with 2014 issues! IEEE Transactions on Haptics (ToH) is a quarterly journal that publishes archival research results related to the science, technology and applications associated with information acquisition and object manipulation through touch.

From the October-December 2014 issue

Incrementally Learning Objects by Touch: Online Discriminative and Generative Models for Tactile-Based Recognition

By Harold Soh and Yiannis Demiris

Free Featured ArticleHuman beings not only possess the remarkable ability to distinguish objects through tactile feedback but are further able to improve upon recognition competence through experience. In this work, we explore tactile-based object recognition with learners capable of incremental learning. Using the sparse online infinite Echo-State Gaussian process (OIESGP), we propose and compare two novel discriminative and generative tactile learners that produce probability distributions over objects during object grasping/palpation. To enable iterative improvement, our online methods incorporate training samples as they become available. We also describe incremental unsupervised learning mechanisms, based on novelty scores and extreme value theory, when teacher labels are not available. We present experimental results for both supervised and unsupervised learning tasks using the iCub humanoid, with tactile sensors on its five-fingered anthropomorphic hand, and 10 different object classes. Our classifiers perform comparably to state-of-the-art methods (C4.5 and SVM classifiers) and findings indicate that tactile signals are highly relevant for making accurate object classifications. We also show that accurate “early” classifications are possible using only 20-30 percent of the grasp sequence. For unsupervised learning, our methods generate high quality clusterings relative to the widely-used sequential k-means and self-organising map (SOM), and we present analyses into the differences between the approaches.

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ToH is a joint publication of the IEEE Computer Society, IEEE Robotics and Automation Society and the IEEE Consumer Electronics Society.

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