  The conditional diffusion model has been demonstrated as an efficient tool
for learning robot policies, owing to its advancement to accurately model the
conditional distribution of policies. The intricate nature of real-world
scenarios, characterized by dynamic obstacles and maze-like structures,
underscores the complexity of robot local navigation decision-making as a
conditional distribution problem. Nevertheless, leveraging the diffusion model
for robot local navigation is not trivial and encounters several under-explored
challenges: (1) Data Urgency. The complex conditional distribution in local
navigation needs training data to include diverse policy in diverse real-world
scenarios; (2) Myopic Observation. Due to the diversity of the perception
scenarios, diffusion decisions based on the local perspective of robots may
prove suboptimal for completing the entire task, as they often lack foresight.
In certain scenarios requiring detours, the robot may become trapped. To
address these issues, our approach begins with an exploration of a diverse data
generation mechanism that encompasses multiple agents exhibiting distinct
preferences through target selection informed by integrated global-local
insights. Then, based on this diverse training data, a diffusion agent is
obtained, capable of excellent collision avoidance in diverse scenarios.
Subsequently, we augment our Local Diffusion Planner, also known as LDP by
incorporating global observations in a lightweight manner. This enhancement
broadens the observational scope of LDP, effectively mitigating the risk of
becoming ensnared in local optima and promoting more robust navigational
decisions.
