Learning Omnidirectional Locomotion for a Salamander-Like Quadruped Robot

TL;DR

This paper introduces a learning framework for salamander-like quadruped robots that enables the autonomous acquisition of diverse, omnidirectional gaits, enhancing flexibility and adaptability beyond predefined patterns.

Contribution

The proposed method allows robots to learn a wide range of gaits without reference motions, leveraging phase variables and symmetry data augmentation for improved exploration and efficiency.

Findings

Robot learned 22 omnidirectional gaits

Gaits exhibit dynamic and symmetric movements

Framework outperforms traditional predefined gait methods

Abstract

Salamander-like quadruped robots are designed inspired by the skeletal structure of their biological counterparts. However, existing controllers cannot fully exploit these morphological features and largely rely on predefined gait patterns or joint trajectories, which prevents the generation of diverse and flexible locomotion and limits their applicability in real-world scenarios. In this paper, we propose a learning framework that enables the robot to acquire a diverse repertoire of omnidirectional gaits without reference motions. Each body part is controlled by a phase variable capable of forward and backward evolution, with a phase coverage reward to promote the exploration of the leg phase space. Additionally, morphological symmetry of the robot is incorporated via data augmentation, improving sample efficiency and enforcing both motion-level and task-level symmetry in learned behaviors. Extensive experiments show that the robot successfully acquires 22 omnidirectional gaits exhibiting both dynamic and symmetric movements, demonstrating the effectiveness of the proposed learning framework.