  Route planning for navigation under partial observability plays a crucial
role in modern robotics and autonomous driving. Existing route planning
approaches can be categorized into two main classes: traditional autoregressive
and diffusion-based methods. The former often fails due to its myopic nature,
while the latter either assumes full observability or struggles to adapt to
unfamiliar scenarios, due to strong couplings with behavior cloning from
experts. To address these deficiencies, we propose a versatile diffusion-based
approach for both 2D and 3D route planning under partial observability.
Specifically, our value-guided diffusion policy first generates plans to
predict actions across various timesteps, providing ample foresight to the
planning. It then employs a differentiable planner with state estimations to
derive a value function, directing the agent's exploration and goal-seeking
behaviors without seeking experts while explicitly addressing partial
observability. During inference, our policy is further enhanced by a
best-plan-selection strategy, substantially boosting the planning success rate.
Moreover, we propose projecting point clouds, derived from RGB-D inputs, onto
2D grid-based bird-eye-view maps via semantic segmentation, generalizing to 3D
environments. This simple yet effective adaption enables zero-shot transfer
from 2D-trained policy to 3D, cutting across the laborious training for 3D
policy, and thus certifying our versatility. Experimental results demonstrate
our superior performance, particularly in navigating situations beyond expert
demonstrations, surpassing state-of-the-art autoregressive and diffusion-based
baselines for both 2D and 3D scenarios.
