Learning Whole-Body Control for a Salamander Robot

TL;DR

This paper presents a reinforcement learning-based approach for whole-body control of a salamander robot, enabling stable terrestrial and aquatic locomotion with transfer from simulation to real-world hardware.

Contribution

It introduces a unified learning-based control framework for salamander robots that transfers from simulation to physical hardware, supporting diverse locomotion modes.

Findings

Achieved stable walking on flat and uneven terrains

Enabled transitions between walking and swimming in simulation

Demonstrated successful sim-to-real transfer of control policies

Abstract

Amphibious legged robots inspired by salamanders are promising in applications in complex amphibious environments. However, despite the significant success of training controllers that achieve diverse locomotion behaviors in conventional quadrupedal robots, most salamander robots relied on central-pattern-generator (CPG)-based and model-based coordination strategies for locomotion control. Learning unified joint-level whole-body control that reliably transfers from simulation to highly articulated physical salamander robots remains relatively underexplored. In addition, few legged robots have tried learning-based controllers in amphibious environments. In this work, we employ Reinforcement Learning to map proprioceptive observations and commanded velocities to joint-level actions, allowing coordinated locomotor behaviors to emerge. To deploy these policies on hardware, we adopt a system-level real-to-sim matching and sim-to-real transfer strategy. The learned controller achieves stable and coordinated walking on both flat and uneven terrains in the real world. Beyond terrestrial locomotion, the framework enables transitions between walking and swimming in simulation, highlighting a phenomenon of interest for understanding locomotion across distinct physical modes.