構成空間クリアランスから特徴空間マージンへ：学習ベース衝突検出のサンプル複雑度
From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection
SVMを用いた衝突検出のサンプル複雑度を理論的に解析し、ロボットのクリアランスに基づく保証付きアルゴリズムを提案した論文。
著者: Sapir Tubul, Aviv Tamar, Kiril Solovey, Oren Salzman
分類: cs.RO
原文アブストラクト
Motion planning is a central challenge in robotics, with learning-based approaches gaining significant attention in recent years. Our work focuses on a specific aspect of these approaches: using machine-learning techniques, particularly Support Vector Machines (SVM), to evaluate whether robot configurations are collision free, an operation termed ``collision detection''. Despite the growing popularity of these methods, there is a lack of theory supporting their efficiency and prediction accuracy. This is in stark contrast to the rich theoretical results of machine-learning methods in general and of SVMs in particular. Our work bridges this gap by analyzing the sample complexity of an SVM classifier for learning-based collision detection in motion planning. We bound the number of samples needed to achieve a specified accuracy at a given confidence level. This result is stated in terms relevant to robot motion-planning such as the system's clearance. Building on these theoretical results, we propose a collision-detection algorithm that can also provide statistical guarantees on the algorithm's error in classifying robot configurations as collision-free or not.
