利用对称性生成真实可行的训练数据，提升机器人行走学习效率
Symmetry-Guided Memory Augmentation for Efficient Locomotion Learning
- 通过机器人与任务对称性生成物理一致的额外训练经验
- 在多种故障和负载变化下实现高效训练且保持鲁棒性能
- 适合需要数据高效的仿生机器人强化学习研究者
为腿式机器人训练强化学习策略通常需要大量环境交互，成本高且耗时。我们提出对称性引导的记忆增强（SGMA）框架，结合结构化经验增强与基于记忆的上下文推理，提升训练效率。该方法利用机器人和任务的对称性，在不增加交互次数的前提下生成物理上一致的额外训练经验。为避免简单增强带来的问题，我们将这些变换延伸至策略的记忆状态，使智能体能够保留任务相关上下文并相应调整行为。我们在四足和人形机器人仿真环境及一台真实四足平台上评估该方法。在包含关节故障和负载变化的多样化行走任务中，该方法实现了高效策略训练并保持了鲁棒性能，展示了数据高效强化学习在腿式机器人中的实用路径。
原文摘要 · Abstract (English)
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.
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