NavDP: Learning Sim-to-Real Navigation Diffusion Policy with Privileged
  Information Guidance
Authors: name: Wenzhe Cai, name: Jiaqi Peng, name: Yuqiang Yang, name: Yujian Zhang, name: Meng Wei, name: Hanqing Wang, name: Yilun Chen, name: Tai Wang, name: Jiangmiao Pang

Learning navigation in dynamic open-world environments is an important yet
challenging skill for robots. Most previous methods rely on precise
localization and mapping or learn from expensive real-world demonstrations. In
this paper, we propose the Navigation Diffusion Policy (NavDP), an end-to-end
framework trained solely in simulation and can zero-shot transfer to different
embodiments in diverse real-world environments. The key ingredient of NavDP's
network is the combination of diffusion-based trajectory generation and a
critic function for trajectory selection, which are conditioned on only local
observation tokens encoded from a shared policy transformer. Given the
privileged information of the global environment in simulation, we scale up the
demonstrations of good quality to train the diffusion policy and formulate the
critic value function targets with contrastive negative samples. Our
demonstration generation approach achieves about 2,500 trajectories/GPU per
day, 20times more efficient than real-world data collection, and results in
a large-scale navigation dataset with 363.2km trajectories across 1244 scenes.
Trained with this simulation dataset, NavDP achieves state-of-the-art
performance and consistently outstanding generalization capability on
quadruped, wheeled, and humanoid robots in diverse indoor and outdoor
environments. In addition, we present a preliminary attempt at using Gaussian
Splatting to make in-domain real-to-sim fine-tuning to further bridge the
sim-to-real gap. Experiments show that adding such real-to-sim data can improve
the success rate by 30\% without hurting its generalization capability.