STEP: 带时空一致性的预热视觉运动策略
STEP: Warm-Started Visuomotor Policies with Spatiotemporal Consistency Prediction
AI总结：
本文提出STEP策略，通过轻量级时空一致性预测机制生成高质量预热动作，同时保持生成能力。引入速度感知扰动注入机制，防止执行停滞，提升实时任务性能。
摘要：
扩散策略最近在机器人操作中崭露头角，因其能建模动作序列分布并捕捉多模态特性。然而，迭代去噪导致显著推理延迟，限制了实时闭环系统的控制频率。现有加速方法或减少采样步数，或直接预测绕过扩散，或重用过去动作，但往往难以同时保持动作质量和实现一致低延迟。本文提出STEP，一种轻量级时空一致性预测机制，用于构建高质量预热动作，这些动作在分布上接近目标动作且在时间上一致，不牺牲原始扩散策略的生成能力。然后，我们提出一种速度感知扰动注入机制，根据时间动作变化自适应调节执行激励，以防止执行停滞，尤其对现实任务至关重要。我们进一步提供理论分析，证明所提预测诱导了局部收缩映射，确保在扩散细化过程中动作误差的收敛。我们在九个模拟基准和两个现实任务上进行了广泛评估。值得注意的是，STEP在2步内可实现RoboMimic基准和现实任务上分别比BRIDGER和DDIM高出21.6%和27.5%的成功率。这些结果表明，STEP在推理延迟和成功率的帕累托前沿上优于现有方法。代码可在https://github.com/Kimho666/STEP上公开获取。
英文摘要：
Diffusion policies have recently emerged as a powerful paradigm for visuomotor control in robotic manipulation due to their ability to model the distribution of action sequences and capture multimodality. However, iterative denoising leads to substantial inference latency, limiting control frequency in real-time closed-loop systems. Existing acceleration methods either reduce sampling steps, bypass diffusion through direct prediction, or reuse past actions, but often struggle to jointly preserve action quality and achieve consistently low latency. In this work, we propose STEP, a lightweight spatiotemporal consistency prediction mechanism to construct high-quality warm-start actions that are both distributionally close to the target action and temporally consistent, without compromising the generative capability of the original diffusion policy. Then, we propose a velocity-aware perturbation injection mechanism that adaptively modulates actuation excitation based on temporal action variation to prevent execution stall especially for real-world tasks. We further provide a theoretical analysis showing that the proposed prediction induces a locally contractive mapping, ensuring convergence of action errors during diffusion refinement. We conduct extensive evaluations on nine simulated benchmarks and two real-world tasks. Notably, STEP with 2 steps can achieve an average 21.6% and 27.5% higher success rate than BRIDGER and DDIM on the RoboMimic benchmark and real-world tasks, respectively. These results demonstrate that STEP consistently advances the Pareto frontier of inference latency and success rate over existing methods.The code is publicly available at https://github.com/Kimho666/STEP.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
