Reference Free Platform Adaptive Locomotion for Quadrupedal Robots using a Dynamics Conditioned Policy

TL;DR

This paper introduces PAL, a unified reinforcement learning-based control method for quadrupedal robots that adapts to different morphologies and dynamics, enabling zero-shot transfer and improved generalization.

Contribution

The paper proposes a novel platform adaptive locomotion approach using dynamics-conditioned policies with two conditioning strategies, enhancing robustness and transferability across diverse robot morphologies.

Findings

Morphology-aware conditioning outperforms temporal dynamics encoding.

Zero-shot transfer achieved across unseen simulated quadrupeds.

Diverse training improves velocity tracking accuracy by up to 30%.

Abstract

This article presents Platform Adaptive Locomotion (PAL), a unified control method for quadrupedal robots with different morphologies and dynamics. We leverage deep reinforcement learning to train a single locomotion policy on procedurally generated robots. The policy maps proprioceptive robot state information and base velocity commands into desired joint actuation targets, which are conditioned using a latent embedding of the temporally local system dynamics. We explore two conditioning strategies - one using a GRU-based dynamics encoder and another using a morphology-based property estimator - and show that morphology-aware conditioning outperforms temporal dynamics encoding regarding velocity task tracking for our hardware test on ANYmal C. Our results demonstrate that both approaches achieve robust zero-shot transfer across multiple unseen simulated quadrupeds. Furthermore, we demonstrate the need for careful robot reference modelling during training: exposing the policy to a diverse set of robot morphologies and dynamics leads to improved generalization, reducing the velocity tracking error by up to 30% compared to the baseline method. Despite PAL not surpassing the best-performing reference-free controller in all cases, our analysis uncovers critical design choices and informs improvements to the state of the art.