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Improving Haptic Adjective Recognition with Unsupervised Feature Learning

2019

Conference Paper

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Humans can form an impression of how a new object feels simply by touching its surfaces with the densely innervated skin of the fingertips. Many haptics researchers have recently been working to endow robots with similar levels of haptic intelligence, but these efforts almost always employ hand-crafted features, which are brittle, and concrete tasks, such as object recognition. We applied unsupervised feature learning methods, specifically K-SVD and Spatio-Temporal Hierarchical Matching Pursuit (ST-HMP), to rich multi-modal haptic data from a diverse dataset. We then tested the learned features on 19 more abstract binary classification tasks that center on haptic adjectives such as smooth and squishy. The learned features proved superior to traditional hand-crafted features by a large margin, almost doubling the average F1 score across all adjectives. Additionally, particular exploratory procedures (EPs) and sensor channels were found to support perception of certain haptic adjectives, underlining the need for diverse interactions and multi-modal haptic data.

Author(s): Benjamin A. Richardson and Katherine J. Kuchenbecker
Book Title: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)
Year: 2019
Month: May

Department(s): Haptische Intelligenz
Research Project(s): Learning Haptic Adjectives from Tactile Data
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

Address: Montreal, Canada
State: Accepted

BibTex

@inproceedings{Richardson19-ICRA-Unsupervised,
  title = {Improving Haptic Adjective Recognition with Unsupervised Feature Learning},
  author = {Richardson, Benjamin A. and Kuchenbecker, Katherine J.},
  booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)},
  address = {Montreal, Canada},
  month = may,
  year = {2019},
  month_numeric = {5}
}