LHNav: Long-Horizon Humanoid Navigation via Multirate Coordination and Physics-Grounded Action Data
Overview
This paper introduces LHNav, a system designed for long-horizon humanoid navigation using multirate coordination and physics-grounded action data. The authors propose an architecture with three roles—Brain for task reasoning, Cerebellum for local motion generation, and Brainstem for whole-body execution—to maintain progress across successive goals while ensuring natural, stable motion. A physics-grounded data engine is developed to train the Cerebellum on collision-aware routes paired with rendered visual observations. In simulation, LHNav outperforms OmniNav and DualVLN in single-goal success and motion smoothness. Using a VLA trained only on single-goal demonstrations, LHNav achieves a 75.56% success rate on long-horizon navigation tasks. Deployment on the Unitree G1 demonstrates zero-shot long-horizon task execution in real-world environments with repositioned objects.
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Reported quantity · Reported success rate: 75.56 percent · reported trials
“t and extend single-goal navigation to successive destinations with natural, route-faithful whole-body motion. In simulation, our navigation VLA outperforms OmniNav and DualVLN in single-goal success and motion smoothness, with fewer collisions and falls. Using a VLA trained only on single-goal demonstrations, LHNav achieves a 75.56% success rate on long-horizon navigation tasks. Deployment on the Unitree G1 further”
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- arXiv Robotics — research abstractsSignalLHNav: Long-Horizon Humanoid Navigation via Multirate Coordination and Physics-Grounded Action Data
This paper introduces LHNav, a system designed for long-horizon humanoid navigation using multirate coordination and physics-grounded action data. The authors propose an architecture with three roles—Brain for task reasoning, Cerebellum for local motion generation, and Brainstem for whole-body execution—to maintain progress across successive goals while ensuring natural, stable motion. A physics-grounded data engine is developed to train the Cerebellum on collision-aware routes paired with rendered visual observations. In simulation, LHNav outperforms OmniNav and DualVLN in single-goal success and motion smoothness. Using a VLA trained only on single-goal demonstrations, LHNav achieves a 75.56% success rate on long-horizon navigation tasks. Deployment on the Unitree G1 demonstrates zero-shot long-horizon task execution in real-world environments with repositioned objects.
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