Fast and Continuous Foothold Adaptation for Dynamic Locomotion through CNNs

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.

arXiv comment9 pages, 11 figures. Accepted to RA-L + ICRA 2019, January 2019

References in corpus (2)

• Simultaneous Contact, Gait and Motion Planning for Robust Multi-Legged Locomotion via Mixed-Integer Convex Optimization
• On-line and on-board planning and perception for quadrupedal locomotion

Cited by in corpus (11)

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• TAMOLS: Terrain-Aware Motion Optimization for Legged Systems
• Robust Legged Robot State Estimation Using Factor Graph Optimization
• ViTAL: Vision-Based Terrain-Aware Locomotion for Legged Robots
• From Pixels to Legs: Hierarchical Learning of Quadruped Locomotion
• Informed Guided Rapidly-Exploring Random Trees*-Connect for Path Planning of Walking Robots
• SafeSteps: Learning Safer Footstep Planning Policies for Legged Robots via Model-Based Priors
• ContactNet: Online Multi-Contact Planning for Acyclic Legged Robot Locomotion
• Real-time Digital Double Framework to Predict Collapsible Terrains for Legged Robots
• Model Predictive Control with Environment Adaptation for Legged Locomotion
• How does the structure embedded in learning policy affect learning quadruped locomotion?