TLDR: A new research paper introduces the Real-Time Iteration Scheme for Diffusion Policy (RTI-DP), a method that significantly accelerates the inference time of diffusion models in robotic manipulation. By leveraging solutions from previous time steps as initial guesses for subsequent iterations, RTI-DP drastically reduces the number of denoising steps required, enabling real-time control without the need for additional training or policy redesign. The approach maintains high performance, even surpassing existing methods in some tasks, and offers theoretical guarantees for stability, making it highly practical for latency-critical robotic applications.
Robotic manipulation has seen remarkable progress thanks to the advent of diffusion models, particularly Diffusion Policy (DP). These models excel at complex tasks, offering improved control, adaptability, and generalization. However, a significant hurdle remains: their slow inference time. Standard diffusion models require hundreds of denoising steps to generate an action, which makes them unsuitable for tasks demanding quick, continuous adjustments, like high-speed or contact-rich manipulations. This slowness can lead to jerky robot movements, reduced productivity, and limits their real-world applicability.
Current efforts to speed up diffusion models often involve distillation techniques, which condense the denoising process into fewer steps, or redesigning the policy structure entirely. While effective, these methods typically demand extensive additional training, which is resource-intensive, especially for large robotic models. This creates a pressing need for solutions that accelerate policy execution without requiring costly retraining or significant model modifications.
In response to this challenge, researchers have introduced a novel approach called the Real-Time Iteration Scheme for Diffusion Policy (RTI-DP). This method draws inspiration from the Real-Time Iteration (RTI) scheme in optimal control, which is known for accelerating optimization by using solutions from previous time steps as initial guesses for subsequent iterations. The core idea behind RTI-DP is to leverage the spatio-temporal consistency inherent in physical systems – the principle that robot actions exhibit continuity and bounded changes over time.
Here’s how RTI-DP works: The very first action prediction is computed using the standard Diffusion Policy with its full denoising process. Since this initial step can be done offline, its computation time isn’t a critical concern. For all subsequent action predictions, RTI-DP takes the outcome of the immediately preceding prediction and uses it as an informed initial guess. This allows the model to refine the action with a significantly reduced number of denoising steps. This iterative refinement process enables the policy to operate efficiently in real-time, one step at a time, while maintaining fast inference and high performance.
A key innovation of RTI-DP is its ability to effectively handle discrete actions, such as grasping, which can otherwise violate the assumption of continuous action changes. The method introduces a scaling-based approach to transform discrete dimensions into a continuous representation, allowing the diffusion model to smoothly interpret and denoise these actions. This ensures trajectory consistency even with abrupt transitions.
The importance of this informed initialization cannot be overstated. While traditional diffusion models start from a generic Gaussian noise, which is mode-agnostic and allows for diverse behaviors, this can lead to instability during real-time execution. By starting inference from a point near the previously predicted action, RTI-DP helps maintain consistency with local behavior modes, preventing oscillations and ensuring stable, goal-oriented movement. This also facilitates a truly closed-loop system, where continuous feedback is incorporated, unlike some prior methods that operate in a partially open-loop manner.
The researchers also provide theoretical conditions for the “contractivity” of the denoising dynamics. This means that small errors in the initial guess at an intermediate step will shrink exponentially as the denoising process progresses, guaranteeing that the final recovered action remains very close to the true action. This theoretical backing helps in estimating the optimal initial denoising step, balancing speed and accuracy.
Extensive simulation experiments were conducted across various robotics benchmarks, including Robomimic, Push-T, and Block-pushing, covering both image-based and state-based control tasks. The results are compelling: RTI-DP significantly reduces inference time while maintaining performance comparable to, and in some cases even surpassing, Diffusion Policy with full denoising steps. For instance, in state-based tasks like Push-T and BlockPush, RTI-DP demonstrated a clear advantage over other acceleration methods, highlighting the benefits of fully closed-loop control.
Compared to other state-of-the-art acceleration methods like Consistency Policy (CP) and Streaming Diffusion Policy (SDP), RTI-DP achieved substantial speedups while preserving strong performance, particularly in precision-critical tasks. The paper also notes that RTI-DP-scale, which applies scaling at the dataset level for discrete actions, generally outperformed RTI-DP-clip, which scales directly on initial guesses, indicating the benefit of preprocessing.
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The implications of RTI-DP are significant. Its ability to achieve substantial speedups without compromising performance or requiring retraining makes it highly suitable for real-time robotic applications. This efficiency could also lower hardware requirements for visuomotor policies, enabling deployment on less powerful GPUs or edge devices without additional training. While the method currently requires multiple denoising steps, future work may explore strategies for one-step denoising without altering the policy structure. For more technical details, you can refer to the full research paper here.
