用一步扩散模型生成高质量机器人运动，兼顾实时性与精度
Robot Motion Planning using One-Step Diffusion with Noise-Optimized Approximate Motions
- 输入图像直接生成近似运动，再通过噪声优化提升质量
- 仅需一步扩散即可生成高保真轨迹，效率远超传统方法
- 噪声按运动分量不确定性动态调整，适合复杂场景规划
本文提出一种基于图像的机器人运动规划方法，采用一步扩散模型。尽管扩散模型能生成高质量运动轨迹，但计算成本过高，难以实现实时控制。为兼顾质量与效率，该方法直接从输入图像预测一个近似运动，并由新型噪声优化器添加加性噪声进行优化。与通用各向同性噪声不同，该噪声优化器根据每个运动分量的不确定性进行各向异性调整。实验表明，本方法在保持一步扩散高效性的同时，性能优于现有最先进方法。
原文摘要 · Abstract (English)
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.
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