AI Insight
This paper introduces SKooP (Symmetric Koopman Predictions), a reinforcement learning method that combines morphological symmetries with Koopman operator theory to improve learning efficiency for legged robot locomotion. The approach learns a mathematical model of robot dynamics alongside the control policy, using the model's predictions to provide smoother, more informative features for training. Testing on bipedal locomotion tasks with a quadruped robot showed that SKooP consistently reduces training time and achieves higher performance compared to standard methods, with policies that transfer well across different simulation environments.
Why it matters
This work addresses a fundamental challenge in robotics: the time and computational cost required to train robots to walk and move effectively. By improving sample efficiency through physics-informed learning, the approach could accelerate the development of more capable legged robots for real-world applications in areas like search and rescue, delivery, and inspection tasks.
Understand the Science
arXiv:2607.11624v3 Announce Type: replace-cross
Abstract: Reinforcement learning (RL) algorithms classically suffer from poor sample efficiency. In robotics, a recent line of work has emerged addressing this problem by encoding physics priors in the learning process. However, most of these approaches are validated on well-defined, low-dimensional benchmark systems rather than high-dimensional robots with complex nonlinear dynamics. In this paper, we introduce textit{SKooP (Symmetric Koopman Predictions)}, an approach combining the advantages of morphological symmetries with those of a Koopman model learned via autoencoder to enhance policy learning. SKooP learns a Koopman model of the system dynamics alongside the policy. The resulting Koopman predictions are used as privileged observations for the critic, allowing the agent to learn based on smoother, more informative features. We also incorporate group symmetries into the actor, critic, encoder and decoder networks to produce a highly equivariant policy. The SKooP approach is validated via in-depth analysis of the learned Koopman models and symmetric policies to showcase how each of these influences the agent’s performance. We also show that the learned policies are transferable to different simulation environments. Our results show that SKooP consistently reduces convergence time and increases the learned reward for multiple challenging bipedal locomotion tasks on a quadruped robot. Project page: https://evelyd.github.io/SymmetricKoopmanPredictions