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.

翻译：扩散模型在机器人学习中的应用日益广泛，但现有设计面临一个明确的权衡。采用动作分块扩散策略（如ManiCM）具有运行快速的优点，然而它们仅能预测短时段的运动片段。这使得这些方法虽具有反应性，却无法捕捉时间相关的运动基元（例如遵循具有内置加减速动态特性的弹簧-阻尼器行为）。近期，运动基元扩散（MPD）通过使用概率动态运动基元（ProDMPs）参数化完整轨迹，部分解决了这一局限性，从而能够生成具有时间结构的运动。然而，MPD直接将运动解码器集成到多步扩散过程中，导致推理延迟过高，限制了其在实时控制场景中的适用性。我们提出FODMP（快速单步运动基元扩散），这是一个将扩散模型蒸馏至ProDMP轨迹参数空间并通过单步解码器生成运动的新框架。FODMP保留运动基元的时间结构，同时通过单步一致性蒸馏消除推理瓶颈。这使得机器人能够以高推理速度执行时间相关基元，适用于基于视觉的闭环控制。在标准操作基准测试（MetaWorld、ManiSkill）中，FODMP的运行速度比MPD快10倍，比动作分块扩散策略快7倍，同时成功率相当或更优。除速度优势外，FODMP通过生成快速加减速运动基元，使机器人能够拦截并安全接住高速飞行的球体，而动作分块扩散策略和MPD因响应过慢难以实现实时拦截。