用位置编码提升机器人自碰撞检测的精度与速度
Improving Machine Learning-Based Robot Self-Collision Checking with Input Positional Encoding
- 在输入向量中加入位置编码，让模型更好捕捉高频细节
- 轻量MLP在低维空间下比传统几何方法更快
- 适合对实时性要求高的机器人运动规划场景
本文研究将计算机图形学中常用的位置编码引入机器学习型自碰撞检测的输入向量。实验表明，融入位置编码可提升分类准确率，使模型更有效捕捉高频变化，从而实现对复杂碰撞模式的精细化表征。结果还显示，基于机器学习的方法（如运行在低维特征空间的轻量多层感知机）相比依赖三角形相交测试和包围体层次结构（BVH）的几何方法，在碰撞检测上具有更快的计算速度。
原文摘要 · Abstract (English)
This manuscript investigates the integration of positional encoding -- a technique widely used in computer graphics -- into the input vector of a binary classification model for self-collision detection. The results demonstrate the benefits of incorporating positional encoding, which enhances classification accuracy by enabling the model to better capture high-frequency variations, leading to a more detailed and precise representation of complex collision patterns. The manuscript shows that machine learning-based techniques, such as lightweight multilayer perceptrons (MLPs) operating in a low-dimensional feature space, offer a faster alternative for collision checking than traditional methods that rely on geometric approaches, such as triangle-to-triangle intersection tests and Bounding Volume Hierarchies (BVH) for mesh-based models.
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