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arXiv · 1809.09759

Fast and Continuous Foothold Adaptation for Dynamic Locomotion through CNNs

Octavio Villarreal·Victor Barasuol·Marco Camurri·Luca Franceschi·Michele Focchi·Massimiliano Pontil·Darwin G. Caldwell·Claudio Semini
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Abstract

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 (CNN). 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.

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Octavio Villarreal, Victor Barasuol, Marco Camurri, Luca Franceschi, Michele Focchi, Massimiliano Pontil, Darwin G. Caldwell, Claudio Semini. 2018-09-25. Fast and Continuous Foothold Adaptation for Dynamic Locomotion through CNNs. https://doi.org/10.1109/lra.2019.2899434

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