FODMP: Fast One-Step Diffusion of Movement Primitives Generation for Time-Dependent Robot Actions

• URL: http://arxiv.org/abs/2603.24806v1
• Date: Wed, 25 Mar 2026 20:38:42 GMT
• Title: FODMP: Fast One-Step Diffusion of Movement Primitives Generation for Time-Dependent Robot Actions
• Abstract summary: Diffusion models are increasingly used for robot learning, but current designs face a clear trade-off.<n>We propose FODMP, a new framework that distills diffusion models into the ProDMPs trajectory parameter space and generates motion using a single-step decoder.
• Score: 8.898076552253583
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 decoder directly into a multi-step diffusion process, resulting in prohibitively high inference latency that limits its applicability in real-time control settings. We propose FODMP (Fast One-step Diffusion of Movement Primitives), a new framework that distills diffusion models into the ProDMPs trajectory parameter space and generates motion using a single-step decoder. FODMP retains the temporal structure of movement primitives while eliminating the inference bottleneck through single-step consistency distillation. This enables robots to execute time-dependent primitives at high inference speed, suitable for closed-loop vision-based control. On standard manipulation benchmarks (MetaWorld, ManiSkill), FODMP runs up to 10 times faster than MPD and 7 times faster than action-chunking diffusion policies, while matching or exceeding their success rates. Beyond speed, by generating fast acceleration-deceleration motion primitives, FODMP allows the robot to intercept and securely catch a fast-flying ball, whereas action-chunking diffusion policy and MPD respond too slowly for real-time interception.

Related papers

• TempoVLA: Learning Speed-Controllable Vision-Language-Action Policies [58.40033352838586]
  Existing Vision-Language-Action models (VLAs) only inherit a single fixed speed from training demonstrations.<n>We observe that the magnitude of each predicted action already governs how fast the robot moves.<n>We turn this observation into TempoVLA, a single VLA whose execution speed is controlled by an explicit condition.
  arXiv  Detail & Related papers  (2026-06-04T17:59:40Z)
• FASTER: Rethinking Real-Time Flow VLAs [82.58822112377923]
  Real-time execution is crucial for deploying Vision-Language-Action (VLA) models in the physical world.<n>We show that reaction time follows a uniform distribution determined jointly by the Time to First Action (TTFA) and the execution horizon.<n>We propose Fast Action Sampling for ImmediaTE Reaction (FASTER) to overcome this issue.
  arXiv  Detail & Related papers  (2026-03-19T17:51:37Z)
• From Flow to One Step: Real-Time Multi-Modal Trajectory Policies via Implicit Maximum Likelihood Estimation-based Distribution Distillation [18.70033095161235]
  We propose a framework that distills a Conditional Flow Matching expert into a fast single-step student via Implicit Likelihood Estimation (IMLE)<n>A bi-directional Chamfer distance provides a set-level objective that promotes both mode coverage and fidelity.<n>A unified perception encoder further integrates multi-view RGB, depth, point clouds, and proprioception into a geometry-aware representation.
  arXiv  Detail & Related papers  (2026-03-10T09:30:05Z)
• Causal Motion Diffusion Models for Autoregressive Motion Generation [19.61051102039212]
  Causal Motion Diffusion Models (CMDM) is a unified framework for autoregressive motion generation.<n> CMDM builds upon a Motion-Language-Aligned Causal VAE (MAC-VAE), which encodes motion sequences into temporally causal latent representations.<n>Experiments on HumanML3D and SnapMoGen demonstrate CMDM outperforms existing diffusion and autoregressive models in both semantic fidelity and temporal smoothness.
  arXiv  Detail & Related papers  (2026-02-26T03:58:25Z)
• OMP: One-step Meanflow Policy with Directional Alignment [26.114675928221974]
  One-step MeanFlow Policy (OMP) designed for high-fidelity, real-time manipulation.<n>Experiments on Adroit and Meta-World benchmarks demonstrate that OMP outperforms state-of-the-art methods in success rate and trajectory accuracy.
  arXiv  Detail & Related papers  (2025-12-22T12:45:35Z)
• Characterizing Motion Encoding in Video Diffusion Timesteps [50.13907856401258]
  We study how motion is encoded in video diffusion timesteps by the trade-off between appearance editing and motion preservation.<n>We identify an early, motion-dominant regime and a later, appearance-dominant regime, yielding an operational motion-appearance boundary in timestep space.
  arXiv  Detail & Related papers  (2025-12-18T21:20:54Z)
• Diffusion Model-based Activity Completion for AI Motion Capture from Videos [2.9271399793140076]
  Current AI motion capture methods rely entirely on observed video sequences, similar to conventional motion capture.<n>We propose a diffusion-model-based action completion technique that generates complementary human motion sequences.<n>By introducing a gate module and a position-time embedding module, our approach achieves competitive results on the Human3.6M dataset.
  arXiv  Detail & Related papers  (2025-05-27T05:04:50Z)
• FRMD: Fast Robot Motion Diffusion with Consistency-Distilled Movement Primitives for Smooth Action Generation [3.7351623987275873]
  We propose Fast Robot Motion Diffusion to generate smooth, temporally consistent robot motions.<n>Our method integrates Movement Primitives (MPs) with Consistency Models to enable efficient, single-step trajectory generation.<n>Our results show that FRMD generates significantly faster, smoother trajectories while achieving higher success rates.
  arXiv  Detail & Related papers  (2025-03-03T20:56:39Z)
• EMDM: Efficient Motion Diffusion Model for Fast and High-Quality Motion Generation [57.539634387672656]
  Current state-of-the-art generative diffusion models have produced impressive results but struggle to achieve fast generation without sacrificing quality. We introduce Efficient Motion Diffusion Model (EMDM) for fast and high-quality human motion generation.
  arXiv  Detail & Related papers  (2023-12-04T18:58:38Z)
• StreamYOLO: Real-time Object Detection for Streaming Perception [84.2559631820007]
  We endow the models with the capacity of predicting the future, significantly improving the results for streaming perception. We consider multiple velocities driving scene and propose Velocity-awared streaming AP (VsAP) to jointly evaluate the accuracy. Our simple method achieves the state-of-the-art performance on Argoverse-HD dataset and improves the sAP and VsAP by 4.7% and 8.2% respectively.
  arXiv  Detail & Related papers  (2022-07-21T12:03:02Z)
• PAN: Towards Fast Action Recognition via Learning Persistence of Appearance [60.75488333935592]
  Most state-of-the-art methods heavily rely on dense optical flow as motion representation. In this paper, we shed light on fast action recognition by lifting the reliance on optical flow. We design a novel motion cue called Persistence of Appearance (PA) In contrast to optical flow, our PA focuses more on distilling the motion information at boundaries.
  arXiv  Detail & Related papers  (2020-08-08T07:09:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.