Fast and Continuous Foothold Adaptation for Dynamic Locomotion Through CNNs

Legged robots can outperform wheeled machines for most navigation tasks across unknown and rough terrains. For such tasks, visual feedback is a fundamental asset to provide robots with terrain awareness. However, robust dynamic locomotion on difficult terrains with real-time performance guarantees r...

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Bibliographic Details
Published in:IEEE robotics and automation letters 2019-04, Vol.4 (2), p.2140-2147
Main Authors: Magana, Octavio Antonio Villarreal, Barasuol, Victor, Camurri, Marco, Franceschi, Luca, Focchi, Michele, Pontil, Massimiliano, Caldwell, Darwin G., Semini, Claudio
Format: Article
Language:English
Subjects:
Adaptation
Airborne/spaceborne computers
Artificial neural networks
Computer simulation
Deep Learning in Robotics and Automation
Feedback
Foot
Landing
Legged locomotion
Legged Robots
Locomotion
Reactive and Sensor-Based Planning
Real time
Robot dynamics
Robot sensing systems
Trajectory
Visual stimuli
Visual tasks
Visualization
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Summary:Legged robots can outperform wheeled machines for most navigation tasks across unknown and rough terrains. For such tasks, visual feedback is a fundamental asset to provide robots with terrain awareness. However, robust dynamic locomotion on difficult terrains with real-time performance guarantees remains a challenge. We present here a real-time, dynamic foothold adaptation strategy based on visual feedback. Our method adjusts the landing position of the feet in a fully reactive manner, using only on-board computers and sensors. The correction is computed and executed continuously along the swing phase trajectory of each leg. To efficiently adapt the landing position, we implement a self-supervised foothold classifier based on a convolutional neural network. Our method results in an up to 200 times faster computation with respect to the full-blown heuristics. Our goal is to react to visual stimuli from the environment, bridging the gap between blind reactive locomotion and purely vision-based planning strategies. We assess the performance of our method on the dynamic quadruped robot HyQ, executing static and dynamic gaits (at speeds up to 0.5 m/s) in both simulated and real scenarios; the benefit of safe foothold adaptation is clearly demonstrated by the overall robot behavior.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2019.2899434