Symmetry-Guided Memory Augmentation for Efficient Locomotion Learning

📰 ArXiv cs.AI

Symmetry-Guided Memory Augmentation improves efficiency of locomotion learning in reinforcement learning

advanced Published 26 Mar 2026

Action Steps

Identify robot and task symmetries to generate additional training data
Implement structured experience augmentation to increase data diversity
Use memory-based context inference to improve policy learning
Evaluate the efficiency of the Symmetry-Guided Memory Augmentation framework in locomotion learning tasks

Who Needs to Know This

Machine learning researchers and roboticists can benefit from this method to improve training efficiency and reduce environment interactions, while software engineers can implement the framework

Key Insight

💡 Leveraging symmetries can generate physically consistent training data and improve reinforcement learning efficiency

Key Takeaways

Symmetry-Guided Memory Augmentation improves efficiency of locomotion learning in reinforcement learning

Full Article

Title: Symmetry-Guided Memory Augmentation for Efficient Locomotion Learning

Abstract:
arXiv:2502.01521v4 Announce Type: replace-cross Abstract: Training reinforcement learning (RL) policies for legged locomotion often requires extensive environment interactions, which are costly and time-consuming. We propose Symmetry-Guided Memory Augmentation (SGMA), a framework that improves training efficiency by combining structured experience augmentation with memory-based context inference. Our method leverages robot and task symmetries to generate additional, physically consistent trainin

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