Where Should I Walk? Predicting Terrain Properties from Images via Self-Supervised Learning

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Date
2019-04Type
- Journal Article
Citations
Cited 49 times in
Web of Science
Cited 56 times in
Scopus
ETH Bibliography
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Abstract
Legged robots have the potential to traverse diverse and rugged terrain. To find a safe and efficient navigation path and to carefully select individual footholds, it is useful to be able to predict properties of the terrain ahead of the robot. In this work, we propose a method to collect data from robot-terrain interaction and associate it to images. Using sparse data acquired in teleoperation experiments with a quadrupedal robot,we train a neural network to generate a dense prediction of the terrain properties in front of the robot. To generate training data, we project the foothold positions from the robot trajectory into on-board camera images. We then attach labels to these footholds by identifying the dominant features of the force-torque signal measured with sensorized feet. We show that data collected in this fashion can be used to train a convolutional network for terrain property prediction as well as weakly supervised semantic segmentation. Finally, we show that the predicted terrain properties can be used for autonomous navigation of the ANYmal quadruped robot. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000323783Publication status
publishedExternal links
Journal / series
IEEE Robotics and Automation LettersVolume
Pages / Article No.
Publisher
IEEEOrganisational unit
09570 - Hutter, Marco / Hutter, Marco
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Show all metadata
Citations
Cited 49 times in
Web of Science
Cited 56 times in
Scopus
ETH Bibliography
yes
Altmetrics