# Geometric Temporal Ensembling

Autonomous lunar rover docking has relied on modular pipelines that split perception, mapping and planning, so perception errors propagate into planning. Researchers trained a vision-language-action policy with a Qwen3.5-4B backbone on safety-aware demonstrations from a privileged expert in the LuPNT lunar simulator, mapping a single monocular camera and a bird's-eye-view map directly to waypoints. Under dust and optical disturbances unseen in training, the policy achieved higher docking success in five of six disturbance conditions, with the largest advantage under the strongest perturbations; adaptive-horizon execution held 90% docking success while cutting policy inferences per successful scenario from 17.16 with a fixed 2 m horizon to 8.36.

Autonomous lunar rover docking has relied on modular pipelines that split perception, mapping and planning, so perception errors propagate into planning. Researchers trained a vision-language-action policy with a Qwen3.5-4B backbone on safety-aware demonstrations from a privileged expert in the LuPNT lunar simulator, mapping a single monocular camera and a bird's-eye-view map directly to waypoints. Under dust and optical disturbances unseen in training, the policy achieved higher docking success in five of six disturbance conditions, with the largest advantage under the strongest perturbations; adaptive-horizon execution held 90% docking success while cutting policy inferences per successful scenario from 17.16 with a fixed 2 m horizon to 8.36.