Event
REACH Hierarchical Humanoid Crawling Framework Released
1 reports · 1 sources
Shengzhang Xu and other authors released REACH—a reinforcement learning-based hierarchical framework that enables the Unitree G1 humanoid robot to traverse height-constrained environments through variable-height bipedal walking and crawling. The authors state that the learned policy regulates body height within complementary command ranges of 1.27–0.50 meters for bipedal walking and 0.50–0.20 meters for crawling, and uses a bidirectional switching policy to connect crouch-walking and crawling. The framework also incorporates a LiDAR planner that converts point clouds into collision-free three-dimensional paths and motion commands. The authors state that real-robot experiments validated height and speed tracking, terrain traversal, mode switching, and traversal of confined spaces; the code and videos have been made public.
Report timeline
- REACH: An RL-based hierarchical humanoid crawling framework for confined-space traversal
REACH is an RL-based hierarchical framework that enables a Unitree G1 humanoid robot to traverse height-constrained environments through variable-height bipedal walking and crawling. Its policies regulate body height over complementary command ranges: 1.27–0.50m for bipedal walking and 0.50–0.20m for crawling, and a bidirectional transition policy connects crouched walking and crawling. The framework also combines a LiDAR planner that converts point clouds into collision-free three-dimensional paths and locomotion commands. Real-world experiments validated height and speed tracking, terrain traversal, mode switching, and confined-space traversal; the code and videos have been released.
arXiv cs.RO ·