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

2026-03-25

AI Summary

Existing diffusion models for robotic motion generation struggle to simultaneously model temporal dependencies and achieve real-time inference, often constrained by short-horizon synthesis or high latency from multi-step sampling. This work proposes distilling diffusion models into the parameter space of Probabilistic Dynamic Movement Primitives (ProDMPs), enabling single-step consistency distillation to generate full-horizon trajectories with realistic acceleration and deceleration dynamics at high speed. The method uniquely supports one-step generation of complete, temporally structured motion primitives, preserving dynamic characteristics while entirely eliminating the multi-step inference bottleneck. Experiments on MetaWorld and ManiSkill benchmarks demonstrate a 10× speedup over MPD and a 7× improvement over action chunking strategies, with comparable or higher task success rates, and enable real-time interception of fast-moving aerial objects.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• diffusion models
• movement primitives
• time-dependent actions
• real-time control
• inference latency

Innovation

• diffusion distillation
• movement primitives
• single-step inference
• real-time robot control
• ProDMPs