Liangyuan Xinchang releases LightNav-0, a zero-shot navigation model deployable across robot morphologies, and Light REACT, a resilient whole-body control policy that recovers from falls and damage, applying the LLM pre-training/alignment/deployment paradigm to embodied AI.
AgentVLN introduces a VLM-as-Brain architecture for vision-language navigation, using cross-space representation mapping to translate 3D paths into 2D visual prompts, context-driven self-correction for error recovery, and query-driven perceptual chain-of-thought for active information gathering, achieving state-of-the-art results on R2R-CE and RxR-CE benchmarks with a 3B model deployable on Jetson edge devices.
CE-Nav introduces a two‑stage imitation‑then‑reinforcement framework that decouples generic geometric planning from robot‑specific dynamics, enabling low‑cost, high‑performance navigation across quadrupeds, humanoids, and drones while requiring only brief online fine‑tuning.
