Imitation Learning in the Deep Learning Era: A Novel Taxonomy and Recent Advances

2025-11-05

AI Summary

Current imitation learning faces core challenges including poor generalization, severe covariate shift, heterogeneous expert data modalities (e.g., partially observable or unlabeled sequences), and the absence of a taxonomy tailored to the deep learning era. This paper presents a systematic survey of deep imitation learning advances since 2015. We propose a novel four-dimensional classification framework centered on expert data modality—namely, state-action pairs, trajectories, observation sequences, and unlabeled sequences—moving beyond traditional paradigms (behavioral cloning, inverse reinforcement learning, adversarial imitation). We critically analyze mainstream methods regarding theoretical assumptions, robustness, and empirical evaluation practices. Integrating recent technical developments, we identify key bottlenecks and articulate concrete future directions. The work provides systematic theoretical foundations and practical guidelines for algorithm design, benchmark construction, and cross-task transfer.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Multiagent Systems: Multiagent Learning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

Imitation learning (IL) enables agents to acquire skills by observing and replicating the behavior of one or multiple experts. In recent years, advances in deep learning have significantly expanded the capabilities and scalability of imitation learning across a range of domains, where expert data can range from full state-action trajectories to partial observations or unlabeled sequences. Alongside this growth, novel approaches have emerged, with new methodologies being developed to address longstanding challenges such as generalization, covariate shift, and demonstration quality. In this survey, we review the latest advances in imitation learning research, highlighting recent trends, methodological innovations, and practical applications. We propose a novel taxonomy that is distinct from existing categorizations to better reflect the current state of the IL research stratum and its trends. Throughout the survey, we critically examine the strengths, limitations, and evaluation practices of representative works, and we outline key challenges and open directions for future research.

Problem

• Developing novel taxonomy for imitation learning advances
• Addressing generalization and covariate shift in imitation learning
• Reviewing methodological innovations across imitation learning domains

Innovation

• Proposing a novel taxonomy for imitation learning
• Reviewing deep learning advances in imitation learning
• Addressing generalization and covariate shift challenges