Falcon: Fast Visuomotor Policies via Partial Denoising
Falcon reuses partial denoising actions based on historical data, achieving 2-7x speedup with negligible performance loss in diffusion policies.
Key Findings
Methodology
Falcon leverages sequential dependencies in visuomotor tasks by reusing partially denoised actions from previous steps, guided by a thresholding mechanism and posterior estimation via Tweedie’s formula. It avoids starting from Gaussian noise at each step, instead initializing from prior denoised actions, significantly reducing sampling steps. The approach integrates seamlessly with existing solvers like DDIM and DPMSolver, functioning as a plugin without retraining. The process involves: referencing previous unexecuted actions, calculating dependency scores, estimating posterior actions conditioned on current observations, and selecting the most suitable partial denoising action for continuation. Extensive validation across 48 simulated environments and 2 real-world robotic tasks demonstrates 2-7x acceleration, with performance comparable to baseline models.
Key Results
- In simulation, Falcon accelerates diffusion policies by 2-7 times, with Lift tasks reaching 7x speedup and complex tasks like Transport achieving about 2x, while maintaining success rates above 95%.
- In real robot experiments, Falcon achieves 2-3x faster action sampling in dexterous grasping and high-precision insertion tasks, with negligible performance degradation.
- Combining Falcon with various solvers (e.g., DDIM, DPMSolver) further reduces the number of function evaluations, outperforming baseline acceleration methods, especially in low-step regimes.
Significance
This work addresses the critical bottleneck of slow inference in diffusion-based visuomotor policies, offering a training-free, plug-in acceleration method that preserves multimodal action expressiveness. It bridges the gap between high-quality policy modeling and real-time decision-making, enabling practical deployment in robotics and autonomous systems. The core innovation lies in exploiting action sequence dependencies, which fundamentally enhances the efficiency and flexibility of diffusion models, paving the way for their broader application in dynamic, real-world environments.
Technical Contribution
Falcon introduces a novel partial denoising mechanism that reuses historical actions based on dependency scores, guided by a thresholding process and posterior estimation. It integrates seamlessly with existing SDE/ODE solvers, functioning as a plug-in without retraining. The method leverages the sequence dependency inherent in visuomotor tasks to reduce sampling steps drastically while maintaining multimodal diversity. Theoretical guarantees are provided through the use of Tweedie’s formula for posterior estimation, ensuring stable and accurate action generation. Empirical results validate its effectiveness across diverse benchmarks and real-world tasks, outperforming traditional acceleration techniques.
Novelty
This is the first work to exploit sequential dependencies in visuomotor diffusion policies for partial denoising, enabling significant acceleration without retraining or performance degradation. Unlike prior methods relying on distillation or large buffers, Falcon dynamically selects starting points based on dependency scores, providing a flexible, plug-in solution. Its innovative use of posterior estimation and thresholding to guide partial denoising sets a new paradigm for efficient, multimodal policy generation in robotics.
Limitations
- In highly dynamic or unpredictable tasks, the dependency assumption weakens, reducing Falcon’s acceleration benefits. Its effectiveness depends on the strength of action sequence correlations.
- In scenarios requiring rapid, large action changes, the reliance on historical data may introduce bias or delay adaptation.
- The method still requires managing a latent buffer and dependency calculations, which may increase computational overhead in very high-dimensional tasks. Further optimization is needed for extreme real-time constraints.
Future Work
Future directions include adaptive thresholding based on task complexity, integrating learning-based dependency estimation, and extending Falcon to multi-agent systems. Additionally, exploring more efficient memory management and real-time deployment in complex, high-dimensional environments will be key to broadening its applicability.
AI Executive Summary
Diffusion models have revolutionized generative tasks, offering rich multimodal action distributions for complex visuomotor control. However, their iterative sampling process, involving multiple denoising steps, severely hampers real-time inference, limiting practical deployment in robotics. Existing acceleration techniques, such as ODE solvers like DDIM and DPMSolver, or distillation-based methods, either require retraining or compromise performance when reducing steps. Addressing this challenge, the present work introduces Falcon, a training-free, plug-in algorithm that exploits the sequential dependencies inherent in visuomotor tasks. By reusing partially denoised actions from previous steps, guided by a thresholding mechanism and posterior estimation via Tweedie’s formula, Falcon significantly reduces the number of sampling steps needed. Extensive experiments across 48 simulated environments and 2 real-world robotic tasks demonstrate that Falcon achieves 2-7x acceleration with negligible performance loss. Notably, it can be combined with existing solvers to further enhance inference speed, making diffusion policies more viable for real-time applications. This advancement opens new avenues for deploying sophisticated visuomotor policies in dynamic, real-world scenarios, bridging the gap between high-fidelity modeling and practical responsiveness. While promising, the method’s reliance on action sequence dependencies suggests limitations in highly stochastic or rapidly changing tasks, warranting future research into adaptive dependency estimation and broader system integration.
Deep Analysis
Background
扩散模型在生成式任务中表现出色，尤其在机器人控制中实现多模态动作建模。代表性工作如DDPM、DDIM和DPMSolver，通过逐步去噪生成高质量动作序列。然而，这些模型的多步采样过程耗时较长，难以满足机器人实时决策的需求。近年来，研究者尝试通过ODE解算器和蒸馏技术缩短采样时间，但常伴性能下降或需重训练，限制了其实际应用。实际场景中，机器人需要快速响应复杂环境，现有技术难以兼顾速度与表现，亟需一种无需训练、高效的加速方案。
Core Problem
核心问题在于扩散策略的多步采样过程导致推理耗时长，难以满足实时控制需求。虽然已有方法缩短采样步数，但在极低步数时性能明显下降，且多依赖重训练或调参，缺乏通用性。如何在保持动作多模态表达的同时，实现快速推理，是当前的关键难题。特别是在动态环境中，动作的序列依赖关系未被充分利用，限制了模型的效率提升空间。解决方案需在保证多样性和准确性的基础上，显著降低采样步骤。
Innovation
提出基于历史动作的部分去噪机制，利用动作的序列依赖关系，减少采样步骤。引入阈值筛选和后验估算，动态选择最优去噪起点，避免每次从高斯噪声采样。该方法无需训练，作为插件结合多种扩散器（如DDIM、DPMSolver），实现高效加速。创新点在于：1）利用动作序列的时间依赖性，增强多模态表现；2）采用阈值机制筛选动作起点；3）结合后验估算，提升动作生成的稳定性和速度。这一机制显著降低采样次数，提升推理效率。
Methodology
- �� 利用前一时刻未执行动作作为参考，计算其与当前动作的依赖度。
- �� 采用Tweedie公式估算后验动作，结合当前观察信息，筛选潜在的去噪起点。
- �� 通过阈值判断，选择最接近目标的历史动作作为起点，避免从零开始。
- �� 从选定起点开始逐步去噪，生成完整动作序列。
- �� 结合不同扩散器（如DDIM、DPMSolver）实现多方案适配，增强灵活性。
- �� 设计优先队列管理历史动作，过滤低噪声动作，保证采样效率。
- �� 在每个时间步，利用存储的动作进行一阶估算，减少采样步骤，提升速度。
Experiments
在48个模拟环境（如RoboMimic、MetaWorld）和2个真实机器人任务（抓取、插入）中验证。比较原始模型与Falcon增强模型的成功率、采样次数（NFE）和速度提升。调节阈值和探索率，进行消融分析。结果显示，Falcon在简单任务中达7倍加速，在复杂任务中约2倍，成功率保持在95%以上。结合多种加速器，效果更佳，验证其广泛适用性。实验证明，Falcon在不同环境中均能显著缩短推理时间，同时保持动作质量。
Results
在模拟环境中，Falcon成功实现2-7倍加速，Lift任务达7倍，复杂任务Transport实现约2倍提升，成功率均超过95%。在真实机器人任务中，Falcon在高精度抓取和插入任务中实现2-3倍加速，动作表现与原模型相当。结合多种扩散器（如DDIM、DPMSolver），整体性能稳定，验证了其广泛适用性。消融分析显示，阈值调节和依赖估算对性能影响显著，合理设置参数能达到最佳平衡。
Applications
该技术适用于机器人自主控制、工业自动化、无人驾驶等实时决策场景。无需重训练，只需在现有扩散策略中插入Falcon模块，即可大幅提升推理速度。未来可结合多模态感知、多智能体系统，推动复杂环境中的自主决策能力，提升工业效率与自主水平。
Limitations & Outlook
在高度动态或随机性强的任务中，动作序列依赖关系减弱，Falcon的加速效果受限。极端低采样步数可能导致性能下降，需优化阈值和估算机制。算法对存储和计算资源有一定要求，未来需提升资源效率。此外，复杂任务中动作变化大，依赖关系不强，需结合其他技术共同提升性能。
Plain Language Accessible to non-experts
想象你在厨房做饭，每次拿锅、切菜都要根据上一次的操作调整。传统方法就像每次都从头开始，重新洗菜、切菜，既慢又麻烦。而Falcon就像记住你之前的操作步骤，利用这些记忆快速做出下一步。它会根据你之前的动作，判断下一步该怎么做，然后只调整少量细节，而不是每次都从零开始。就像厨师凭经验，快速完成一道菜，既快又好吃。这种方法让机器人也能像厨师一样，快速反应，完成复杂任务，节省时间，效率大大提高。
ELI14 Explained like you're 14
想象你在玩一款游戏，每次都要从头操作，花费很多时间。而如果你记住之前的动作，下次就可以直接用这些记忆快速继续，比如你刚刚跳过一座桥，下次遇到类似场景，就不用重新计算跳跃动作。Falcon就像这样聪明，它会记住之前的动作，然后用这些记忆帮忙做下一步。这样一来，机器人也能更快反应，完成任务就像你玩游戏一样快！它通过利用之前的经验，让每次动作都变得更快、更聪明。
Abstract
Diffusion policies are widely adopted in complex visuomotor tasks for their ability to capture multimodal action distributions. However, the multiple sampling steps required for action generation significantly harm real-time inference efficiency, which limits their applicability in real-time decision-making scenarios. Existing acceleration techniques either require retraining or degrade performance under low sampling steps. Here we propose Falcon, which mitigates this speed-performance trade-off and achieves further acceleration. The core insight is that visuomotor tasks exhibit sequential dependencies between actions. Falcon leverages this by reusing partially denoised actions from historical information rather than sampling from Gaussian noise at each step. By integrating current observations, Falcon reduces sampling steps while preserving performance. Importantly, Falcon is a training-free algorithm that can be applied as a plug-in to further improve decision efficiency on top of existing acceleration techniques. We validated Falcon in 48 simulated environments and 2 real-world robot experiments. demonstrating a 2-7x speedup with negligible performance degradation, offering a promising direction for efficient visuomotor policy design.