AI RESEARCH
FODMP: Fast One-Step Diffusion of Movement Primitives Generation for Time-Dependent Robot Actions
arXiv CS.AI
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ArXi:2603.24806v1 Announce Type: cross Diffusion models are increasingly used for robot learning, but current designs face a clear trade-off. Action-chunking diffusion policies like ManiCM are fast to run, yet they only predict short segments of motion. This makes them reactive, but unable to capture time-dependent motion primitives, such as following a spring-damper-like behavior with built-in dynamic profiles of acceleration and deceleration.