embodied-navigation

LightNav-0 is a compact generalist navigation model that leverages pretrained vision-language models' spatial intelligence to achieve state-of-the-art embodied navigation across diverse tasks and robot embodiments.

TAMP-Nav is a unified framework that enhances embodied navigation by aligning vision-language models with 2D visual prompting, selective reasoning with compressed memory, and policy optimization, achieving state-of-the-art performance with high runtime and sample efficiency.

Image2Sim is a neural simulation framework that creates high-fidelity interactive environments from RGB-D images, enabling scalable training for embodied navigation agents. It generates nearly 20K scenes and over 10 million training samples, showing strong benchmark improvements and effective real-world zero-shot transfer.

PlatonicNav introduces a training-free framework for embodied navigation that uses vision-only semantic maps and blind matching to ground language goals, achieving generalization across tasks and embodiments without explicit cross-modal training.

AtlasVA is a teacher-free visual skill memory framework for vision-language model agents that uses spatial heatmaps, visual exemplars, and symbolic text skills to improve spatial decision-making in long-horizon tasks, outperforming baselines on several benchmarks.