FODMP: Fast One-Step Diffusion of Movement Primitives Generation for Time-Dependent Robot Actions
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. Recently, Movement Primitive Diffusion (MPD) partially addresses this limitation by parameterizing full trajectories using Probabilistic Dynamic Movement Primitives (ProDMPs), thereby enabling the generation of temporally structured motions. Nevertheless, MPD integrates the motion decod...
| Authors | Xirui Shi, Arya Ebrahimi, Yi Hu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 25 March 2026 |
| Last updated at source | 25 March 2026 |
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