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.
On-device diffusion transformer policy for efficient robot manipulation
3 Pith papers cite this work. Polarity classification is still indexing.
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CF-VLA uses a coarse initialization over endpoint velocity followed by single-step refinement to achieve strong performance with low inference steps on CALVIN, LIBERO, and real-robot tasks.
A rectified flow model trained on 30 actuation-space demonstrations produces control sequences that yield 97.5% grasp success across the workspace, with generalization to object size changes of ±33% and execution speed scaling from 20% to 200%.
citing papers explorer
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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.
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CF-VLA: Efficient Coarse-to-Fine Action Generation for Vision-Language-Action Policies
CF-VLA uses a coarse initialization over endpoint velocity followed by single-step refinement to achieve strong performance with low inference steps on CALVIN, LIBERO, and real-robot tasks.
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Lightweight Learning from Actuation-Space Demonstrations via Flow Matching for Whole-Body Soft Robotic Grasping
A rectified flow model trained on 30 actuation-space demonstrations produces control sequences that yield 97.5% grasp success across the workspace, with generalization to object size changes of ±33% and execution speed scaling from 20% to 200%.