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Robot Motion Planning using One-Step Diffusion with Noise-Optimized Approximate Motions

Tomoharu Aizu, Takeru Oba, Yuki Kondo, Norimichi Ukita

TL;DR

This work addresses real-time image-based robot motion planning by reducing the diffusion-denoising process to a single step without sacrificing accuracy. It introduces NO-Diffusion, a one-step diffusion framework that uses Noise-Optimized Approximate Motion (NOAM), sampling the initial motion from an observation-conditioned anisotropic Gaussian and denoising once with a lightweight model. End-to-end training ties the observation encoder, the noise estimators, and the diffusion model together, achieving high task success with inference times as low as $0.052$ s on a TITAN RTX. The approach delivers competitive performance against multi-step baselines on the robomimic dataset (Square, Lift, Can) while enabling real-time control, with ablations highlighting the benefits of shared encoders, anisotropic noise, and auxiliary losses. This work supports practical deployment of diffusion-based planning in real-time robotic systems and motivates further exploration of sim-to-real transfer and robustness to varied tasks and camera setups.

Abstract

This paper proposes an image-based robot motion planning method using a one-step diffusion model. While the diffusion model allows for high-quality motion generation, its computational cost is too expensive to control a robot in real time. To achieve high quality and efficiency simultaneously, our one-step diffusion model takes an approximately generated motion, which is predicted directly from input images. This approximate motion is optimized by additive noise provided by our novel noise optimizer. Unlike general isotropic noise, our noise optimizer adjusts noise anisotropically depending on the uncertainty of each motion element. Our experimental results demonstrate that our method outperforms state-of-the-art methods while maintaining its efficiency by one-step diffusion.

Robot Motion Planning using One-Step Diffusion with Noise-Optimized Approximate Motions

TL;DR

This work addresses real-time image-based robot motion planning by reducing the diffusion-denoising process to a single step without sacrificing accuracy. It introduces NO-Diffusion, a one-step diffusion framework that uses Noise-Optimized Approximate Motion (NOAM), sampling the initial motion from an observation-conditioned anisotropic Gaussian and denoising once with a lightweight model. End-to-end training ties the observation encoder, the noise estimators, and the diffusion model together, achieving high task success with inference times as low as s on a TITAN RTX. The approach delivers competitive performance against multi-step baselines on the robomimic dataset (Square, Lift, Can) while enabling real-time control, with ablations highlighting the benefits of shared encoders, anisotropic noise, and auxiliary losses. This work supports practical deployment of diffusion-based planning in real-time robotic systems and motivates further exploration of sim-to-real transfer and robustness to varied tasks and camera setups.

Abstract

This paper proposes an image-based robot motion planning method using a one-step diffusion model. While the diffusion model allows for high-quality motion generation, its computational cost is too expensive to control a robot in real time. To achieve high quality and efficiency simultaneously, our one-step diffusion model takes an approximately generated motion, which is predicted directly from input images. This approximate motion is optimized by additive noise provided by our novel noise optimizer. Unlike general isotropic noise, our noise optimizer adjusts noise anisotropically depending on the uncertainty of each motion element. Our experimental results demonstrate that our method outperforms state-of-the-art methods while maintaining its efficiency by one-step diffusion.
Paper Structure (18 sections, 3 equations, 7 figures, 3 tables)

This paper contains 18 sections, 3 equations, 7 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Diffusion Models
  4. Diffusion Models for Motion Generation
  5. Diffusion Step Reduction
  6. Problem Definition and Notation
  7. NO-Diffusion: One-step Diffusion with Noise-Optimized Approximate Motion
  8. Overview
  9. Noise-optimized Approximate Motion Estimator (NOAM)
  10. Training
  11. Experimental Results
  12. Dataset
  13. Training Details
  14. Quantitative Evaluation
  15. Comparison of Motion Prediction Time
  16. ...and 3 more sections

Figures (7)

  • Figure 1: Previous methods Diffusion_Policybridger_chen2024behavioral vs. our proposed method.
  • Figure 2: Input and output data in our method.
  • Figure 3: Overview of our method, NO-Diffusion, and the detail of our proposed Noise-optimized Approximate Motion Estimator, NOAM.
  • Figure 4: Square
  • Figure 5: Lift
  • ...and 2 more figures