Efficient Diffusion Planning with Temporal Diffusion
Diffusion-based planning methods suffer from high computational overhead, low decision frequency, and susceptibility to plan–reality discrepancies due to frequent full replanning. To address these issues, we propose the Temporal Diffusion Planner (TDP). TDP distributes the denoising process across the temporal dimension, enabling progressive, stepwise refinement of a blurred long-horizon plan—eliminating the need for per-step full replanning. It further introduces a state-consistency-driven automatic replanning strategy that enhances real-world alignment while preserving planning continuity. By integrating offline reinforcement learning with dynamic temporal denoising, TDP significantly reduces computational burden. On the D4RL benchmark, TDP achieves an 11–24.8× increase in decision frequency over baseline diffusion planners, while matching or exceeding their task performance.