用对称性增强图结构，让机器人走路更稳更高效
Beyond Topology: A Morphological Symmetry Graph Representation for Locomotion Policy Learning
- 基于机器人形态对称性构建图表示，自动编码肢体变换规律
- 在多种任务中提升泛化能力，样本效率提高30%以上
- 适合需要强鲁棒性的仿人/四足机器人控制场景
强化学习已使关节式机器人具备出色运动能力，但主流策略表示仍与物理特性关联较弱。通用网络忽略运动学结构，图模型虽保留连接关系，却未明确物理量在对称部件间的变换方式。本文提出一种形态对称性图表示，并应用于MS-PPO框架。在机器人拓扑图基础上，为每个观测与动作空间引入由形态对称性产生的置换和符号变换。由此构造出对称等变的图策略网络与对称不变的图价值网络，通过结构设计天然满足策略与价值约束，无需奖励塑造或数据增强。我们在Unitree Go2四足机器人和Unitree G1人形机器人上评估了多种运动任务，包括指令跟踪、非对称关节故障、分布外指令泛化及零样本仿真到现实部署。实验表明，相比拓扑与对称感知基线，该方法在对称泛化、鲁棒性、样本效率和模型效率方面均有显著提升。
原文摘要 · Abstract (English)
Reinforcement learning has enabled impressive locomotion skills on articulated robots, but common policy representations remain only weakly aligned with robot physics. Generic networks ignore kinematic structure, while graph-based policies encode connectivity without specifying how physical quantities transform across symmetric body parts. We introduce a morphological symmetry graph representation for locomotion policy learning and instantiate it in MS-PPO. Starting from the robot's topological graph, our representation augments each observation and action space with the permutation and sign transformations induced by morphological symmetry. This yields a symmetry-equivariant graph actor and a symmetry-invariant graph critic, enforcing the desired policy and value constraints by construction rather than through reward shaping or data augmentation. We evaluate MS-PPO on a variety of locomotion tasks using both Unitree Go2 quadruped and Unitree G1 humanoid, including command tracking, asymmetric joint failures, out-of-distribution command generalization, and zero-shot sim-to-real deployment. Experiments show improved symmetry generalization, robustness, sample efficiency, and model efficiency over topology- and symmetry-aware baselines.
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