Mixed-Density Diffuser: Efficient Planning with Non-uniform Temporal Resolution

• URL: http://arxiv.org/abs/2510.23026v2
• Date: Mon, 03 Nov 2025 17:17:23 GMT
• Title: Mixed-Density Diffuser: Efficient Planning with Non-uniform Temporal Resolution
• Authors: Crimson Stambaugh, Rajesh P. N. Rao,
• Abstract summary: Training models to skip steps in their trajectories helps capture long-term dependencies without additional or memory computational cost.<n>We hypothesize this temporal density threshold is non-uniform across a temporal horizon and that certain parts of a planned trajectory should be more densely planned.
• Score: 1.1172382217477128
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent studies demonstrate that diffusion planners benefit from sparse-step planning over single-step planning. Training models to skip steps in their trajectories helps capture long-term dependencies without additional or memory computational cost. However, predicting excessively sparse plans degrades performance. We hypothesize this temporal density threshold is non-uniform across a temporal horizon and that certain parts of a planned trajectory should be more densely planned. We propose Mixed Density Diffuser (MDD), a diffusion planner where the densities throughout the horizon are tunable hyperparameters. MDD achieves a new SOTA across the Maze2D, Franka Kitchen, and Antmaze D4RL task domains.

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