Muninn accelerates diffusion trajectory planners up to 4.6x by spending an uncertainty budget to decide when to cache denoiser outputs, preserving performance and certifying bounded deviation from full computation.
Multi-robot mo- tion planning with diffusion models
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.RO 3roles
background 1polarities
background 1representative citing papers
A flow-matching generator plus a learned warm-started safety filter plans collision-free trajectories for tens of robots in tens of milliseconds, with batched multi-instance support.
DIFF-IPPO integrates open-vocabulary belief maps with a diffusion planner to produce trajectories that concentrate coverage on high-belief regions, reporting normalized detection scores of 81.49-86.55% and 3.5-minute first detections with five drones in simulation.
citing papers explorer
-
Muninn: Your Trajectory Diffusion Model But Faster
Muninn accelerates diffusion trajectory planners up to 4.6x by spending an uncertainty budget to decide when to cache denoiser outputs, preserving performance and certifying bounded deviation from full computation.
-
Flow-Opt: Scalable Centralized Multi-Robot Trajectory Optimization with Flow Matching and Differentiable Optimization
A flow-matching generator plus a learned warm-started safety filter plans collision-free trajectories for tens of robots in tens of milliseconds, with batched multi-instance support.
-
DIFF-IPPO: Diffusion-Based Informative Path Planning with Open-Vocabulary Belief Maps
DIFF-IPPO integrates open-vocabulary belief maps with a diffusion planner to produce trajectories that concentrate coverage on high-belief regions, reporting normalized detection scores of 81.49-86.55% and 3.5-minute first detections with five drones in simulation.