Event-Triggered Variable-Step Diffusion Sampling based on Force-Derivative Feedback will reduce inference latency without compromising manipulation precision.

Motivation

The 'Structural Slow-Fast' architecture handles input frequencies, but the output diffusion generation typically uses a fixed number of denoising steps. However, non-contact phases require less precision than contact phases. Dynamically adjusting the diffusion compute budget based on the 'Fast' (force) signal could optimize the speed-accuracy trade-off.

Proposed Method

Implement a dynamic inference loop where the number of diffusion denoising steps is a function of the rate of change in the force sensor readings (force derivative). During free motion (zero force change), use a fast, low-step scheduler (e.g., DDIM with 2 steps). Upon detecting contact transients (high force derivative), switch to a high-precision scheduler (e.g., 10-20 steps) to resolve complex contact dynamics accurately.

Expected Contribution

A mechanism to significantly increase the control frequency of diffusion policies in real-time robotics, addressing the slow inference bottleneck of diffusion models.

Required Resources

High-frequency control loop setup, GPU for real-time inference optimization.

Source Paper

ImplicitRDP: An End-to-End Visual-Force Diffusion Policy with Structural Slow-Fast Learning

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