Learning Quadruped Locomotion Policies using Logical Rules

Abstract

Quadruped animals are capable of exhibiting a diverse range of locomotion gaits. While progress has been made in demonstrating such gaits on robots, current methods rely on motion priors, dynamics models, or other forms of extensive manual efforts. People can use natural language to describe dance moves. Could one use a formal language to specify quadruped gaits? To this end, we aim to enable easy gait specification and efficient policy learning. Leveraging Reward Machines (RMs) for high-level gait specification over foot contacts, our approach is called RM-based Locomotion Learning (RMLL), and supports adjusting gait frequency at execution time. Gait specification is enabled through the use of a few logical rules per gait (e.g., alternate between moving front feet and back feet) and does not require labor-intensive motion priors. Experimental results in simulation highlight the diversity of learned gaits (including two novel gaits), their energy consumption and stability across different terrains, and the superior sample-efficiency when compared to baselines. We also demonstrate these learned policies with a real quadruped robot.

Video

Overview

Overview of RM-based Locomotion Learning (RMLL). We consider propositional statements specifying foot contacts. We then construct an automaton via LTL formulas over propositional statements for each locomotion gait (left side). To train gait-specific locomotion policies, we use observations which contain information from the RM, proprioception, velocity and gait frequency commands, and variables from a state estimator (right side).

Results

Reward curves for all gaits. RMLL more efficiently accumulates reward for each gait, particularly for the gaits with more complex foot contact sequences Walk, Three-One, and Half-Bound.