X-IL: Exploring the Design Space of Imitation Learning Policies

2025-02-17

AI Summary

Imitation learning (IL) policy design involves a vast, unexplored design space encompassing feature encoding schemes, neural architectures, and optimization paradigms. Method: We propose X-IL, the first full-stack modular and plug-and-play IL framework, enabling flexible substitution of feature encoders, diverse backbone architectures (e.g., Transformer, Mamba, xLSTM), and generative optimization methods (e.g., score matching, flow matching). Contribution/Results: We systematically evaluate hundreds of configurations across mainstream robotic IL benchmarks. Our analysis reveals novel high-performing combinations surpassing state-of-the-art results and uncovers principled performance trade-offs among components. To foster reproducibility and standardization in IL policy engineering, we open-source a comprehensive configuration library and an empirical analysis guide with validated implementations.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Explores imitation learning policy design
• Develops modular framework X-IL
• Discovers superior policy configurations

Innovation

• Modular open-source framework
• Swappable policy components
• Novel policy configurations discovery