X-IL: 模倣学習ポリシーの設計空間を探る
X-IL: Exploring the Design Space of Imitation Learning Policies
模倣学習ポリシーの構成要素を自由に組み替えられるオープンソースフレームワークX-ILを提案し、設計空間を体系的に探索して既存手法を上回る新構成を発見した。
著者: Xiaogang Jia, Atalay Donat, Xi Huang, Xuan Zhao, Denis Blessing, Hongyi Zhou, Han A. Wang, Hanyi Zhang, Qian Wang, Rudolf Lioutikov, Gerhard Neumann
分類: cs.RO, cs.LG
原文アブストラクト
Designing modern imitation learning (IL) policies requires making numerous decisions, including the selection of feature encoding, architecture, policy representation, and more. As the field rapidly advances, the range of available options continues to grow, creating a vast and largely unexplored design space for IL policies. In this work, we present X-IL, an accessible open-source framework designed to systematically explore this design space. The framework's modular design enables seamless swapping of policy components, such as backbones (e.g., Transformer, Mamba, xLSTM) and policy optimization techniques (e.g., Score-matching, Flow-matching). This flexibility facilitates comprehensive experimentation and has led to the discovery of novel policy configurations that outperform existing methods on recent robot learning benchmarks. Our experiments demonstrate not only significant performance gains but also provide valuable insights into the strengths and weaknesses of various design choices. This study serves as both a practical reference for practitioners and a foundation for guiding future research in imitation learning.
