Symmetry-Guided Memory Augmentation for Efficient Locomotion Learning
Summary: 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 training experiences without requiring extra interactions. To avoid the pitfalls of naive augmentation, we extend these transformations to the policy’s memory states, enabling the agent to retain task-relevant context and adapt its behavior accordingly. We evaluate the approach on quadruped and humanoid robots in simulation, as well as on a real quadruped platform. Across diverse locomotion tasks involving joint failures and payload variations, our method achieves efficient policy training while maintaining robust performance, demonstrating a practical route toward data-efficient RL for legged robots.
Introduction
Legged robots have the potential to navigate complex environments where wheeled robots may struggle. However, training these robots using reinforcement learning (RL) often demands a vast number of interactions with their environments. The costs associated with this process, both in terms of time and resources, can be prohibitive. The introduction of Symmetry-Guided Memory Augmentation (SGMA) aims to mitigate these challenges by enhancing the efficiency of training protocols.
Methodology
The SGMA framework is built upon two key concepts: structured experience augmentation and memory-based context inference. By leveraging the inherent symmetries of both the robot and the tasks it undertakes, SGMA can generate additional training experiences that are both physically plausible and contextually relevant. This approach circumvents the need for additional real-world interactions, significantly reducing the overall training time.
Key Features
- Structured Experience Augmentation: Generates new training scenarios based on existing experiences, enriching the learning dataset.
- Memory-Based Context Inference: Allows the agent to maintain task-relevant context through its memory states, ensuring adaptive behavior in varied situations.
- Symmetry Utilization: Exploits robot and task symmetries to create physically consistent experiences, enhancing the learning process without additional real-world interaction.
Results and Evaluation
SGMA was evaluated through simulations on both quadruped and humanoid robots, as well as on a real-world quadruped platform. The results demonstrated that SGMA not only led to efficient policy training but also maintained robust performance across a range of locomotion tasks. These tasks included scenarios with joint failures and varying payloads, showcasing the versatility and effectiveness of the SGMA framework.
Conclusion
The SGMA approach presents a promising pathway towards achieving data-efficient reinforcement learning for legged robots. By harnessing the power of symmetry and memory augmentation, this innovative methodology significantly reduces the need for extensive environment interactions, paving the way for more practical and scalable applications in robotic locomotion.