AllGaits: Learning All Quadruped Gaits and Transitions
Guillaume Bellegarda, Milad Shafiee, Auke Ijspeert

TL;DR
This paper introduces a unified deep reinforcement learning framework that enables quadruped robots to learn, switch between, and optimize all common gaits and transitions for energy-efficient locomotion across different speeds.
Contribution
It presents a novel DRL-based policy controlling a CPG system to produce and transition between all quadruped gaits, including energy optimization and robustness to leg failures.
Findings
The framework can generate all 9 typical quadruped gaits.
Gait transitions can be instantaneously controlled at any velocity.
The approach demonstrates robustness to leg failures and generalizes to unseen gaits.
Abstract
We present a framework for learning a single policy capable of producing all quadruped gaits and transitions. The framework consists of a policy trained with deep reinforcement learning (DRL) to modulate the parameters of a system of abstract oscillators (i.e. Central Pattern Generator), whose output is mapped to joint commands through a pattern formation layer that sets the gait style, i.e. body height, swing foot ground clearance height, and foot offset. Different gaits are formed by changing the coupling between different oscillators, which can be instantaneously selected at any velocity by a user. With this framework, we systematically investigate which gait should be used at which velocity, and when gait transitions should occur from a Cost of Transport (COT), i.e. energy-efficiency, point of view. Additionally, we note how gait style changes as a function of locomotion speed for…
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Taxonomy
TopicsRobotic Locomotion and Control · Reinforcement Learning in Robotics · Human Motion and Animation
