Multidimensional Time-Series Shapelets Reliably Detect and Classify Contact Events in Force Measurements of Wiping Actions
| Autor: | Georg Bartels, Michael Beetz, Simon Stelter |
|---|---|
| Jazyk: | angličtina |
| Předmět: |
0209 industrial biotechnology
Engineering Control and Optimization Series (mathematics) Recall business.industry Event (computing) Mechanical Engineering Biomedical Engineering 02 engineering and technology Cross-validation Computer Science Applications Human-Computer Interaction 020901 industrial engineering & automation Artificial Intelligence Control and Systems Engineering 020204 information systems 0202 electrical engineering electronic engineering information engineering Torque sensor Robot Computer vision Computer Vision and Pattern Recognition Artificial intelligence business |
| Zdroj: | IEEE Robotics and Automation Letters |
| ISSN: | 2377-3766 2377-3774 |
| DOI: | 10.1109/lra.2017.2716423 |
| Popis: | The vision of service robots that autonomously manipulate objects as skillfully and flexibly as humans is still an open challenge. Findings from cognitive psychology suggest that the human brain structures manipulation actions along representations of contact events and their perceptually distinctive sensory signals. In this letter, we investigate how to reliably detect and classify contact events during robotic wiping actions. We present an algorithm that learns the distinct shapes of force measurements during contact events using multidimensional time-series shapelets. We evaluate our approach on a dataset consisting of 460 real-world robot wiping episodes that we collected using a table-mounted robot with a wrist-mounted force/torque sensor. Our approach shows good performance with tenfold cross validation yielding 97.5% precision and 99.3% recall, and can also be used for online contact event detection and classification. |
| Databáze: | OpenAIRE |
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