JEPLO: Joint-Embedding Predictive Learning for LiDAR-Based Legged Locomotion

2026-09-14

AI Summary

该研究提出JEPLO框架，利用LiDAR数据通过联合嵌入预测学习方法实现腿部机器人无需地图的感知运动，解决了LiDAR在腿部机器人移动中应用不足的问题。

Abstract

Light detection and ranging (LiDAR) remains less explored than RGB-D sensing for perceptive legged locomotion, and existing LiDAR-based approaches often rely on explicit mapping. We present JEPLO (Joint-Embedding Predictive learning for legged LOcomotion), a single-stage learning framework for mapping-free, LiDAR-based perceptive locomotion for legged robots. We introduce a proprio-exteroceptive JEPA (PE-JEPA) world model to learn predictive egocentric terrain representations from onboard observations, including raw LiDAR scans. A concurrent JEPA-teacher-student (CJTS) pipeline is further proposed to train a locomotion policy informed by JEPA latent representations in simulation using deep reinforcement learning with a simple reward formulation. The framework achieves successful sim-to-real transfer, enabling omnidirectional traversal of diverse terrains, including long staircases and high boxes, with lightweight onboard computation. Evaluations demonstrate greater robustness than existing perceptive locomotion frameworks, particularly under degraded perception caused by occlusion, sparsity and noise. Further analysis validates JEPLO's ability to retain task-relevant information under these challenging conditions. We open-source our implementation, experimental datasets, and hardware setup designs https://github.com/ASIG-X/JEPLO.

Problem

• LiDAR
• Legged Locomotion
• Perceptive Navigation
• Degraded Perception
• Mapping-Free

Innovation

• Joint-Embedding Predictive Learning
• LiDAR-based Locomotion
• Mapping-free
• Proprio-exteroceptive JEPA
• CJTS Pipeline