ViTAL: Vision-Based Terrain-Aware Locomotion for Legged Robots

TL;DR

This paper introduces ViTAL, a vision-based terrain-aware locomotion strategy for legged robots that improves foothold and pose planning by maximizing the reachability of safe footholds, validated on HyQ robots climbing complex terrains.

Contribution

ViTAL presents a novel pose adaptation paradigm that optimizes robot body pose to enhance foothold reachability, advancing terrain-aware locomotion planning.

Findings

ViTAL outperforms baseline strategies in obstacle climbing tasks.

Robots successfully navigate stairs, gaps, and rough terrains.

ViTAL demonstrates effective terrain adaptability across different gaits.

Abstract

This work is on vision-based planning strategies for legged robots that separate locomotion planning into foothold selection and pose adaptation. Current pose adaptation strategies optimize the robot's body pose relative to given footholds. If these footholds are not reached, the robot may end up in a state with no reachable safe footholds. Therefore, we present a Vision-Based Terrain-Aware Locomotion (ViTAL) strategy that consists of novel pose adaptation and foothold selection algorithms. ViTAL introduces a different paradigm in pose adaptation that does not optimize the body pose relative to given footholds, but the body pose that maximizes the chances of the legs in reaching safe footholds. ViTAL plans footholds and poses based on skills that characterize the robot's capabilities and its terrain-awareness. We use the 90 kg HyQ and 140 kg HyQReal quadruped robots to validate ViTAL, and show that they are able to climb various obstacles including stairs, gaps, and rough terrains at different speeds and gaits. We compare ViTAL with a baseline strategy that selects the robot pose based on given selected footholds, and show that ViTAL outperforms the baseline.