让机器人通过对称性先验实现更高效的极限攀爬
SWAP: Symmetric Equivariant World-Model for Agile Robot Parkour
- 在世界模型和控制网络中直接嵌入左右对称性
- 实测跨越2.13米间隙，攀爬1.63米平台创纪录
- 可零样本迁移至未见镜像地形，适合复杂户外场景
尽管潜在世界模型能实现极端攀爬所需的前瞻预测，但其纯数据驱动的特性导致左右对称交互被重复编码为独立模式，增加学习负担并阻碍几何规律捕捉，限制潜在空间对下游策略的效率。为此，我们提出SWAP，一种端到端的对称等变世界模型。该框架将对称性直接嵌入世界模型和演员-评论家网络中。在真实世界测试中，机器人成功跃过2.13米宽的间隙，并攀上1.63米高的平台，打破四足机器人攀爬记录。此外，该框架在未见过的镜像地形上表现出鲁棒的几何泛化能力，以及在多种户外环境中出色的零样本迁移性能。结果表明，对称等变性是推动学习腿式运动物理边界的有效结构先验。
原文摘要 · Abstract (English)
While latent world models enable the proactive predictions required for extreme parkour, their purely data-driven nature forces them to redundantly encode left-right symmetric interactions as independent patterns. This inflates the learning burden and hinders the capture of geometric regularities, restricting the latent space's efficiency for downstream policies. To address this, we propose SWAP, an end-to-end equivariant symmetric world model. This framework embeds symmetry directly into both the world model and the actor-critic networks. In real-world tests, the robot leaps across a 2.13 m gap and climbs a 1.63 m platform, breaking records for quadruped parkour. Furthermore, the framework exhibits robust geometric generalization to unseen mirrored terrains and exceptional zero-shot transferability across diverse outdoor environments. These results demonstrate that symmetry equivariance is an effective structural prior for pushing the physical boundaries of learned legged locomotion.
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