A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges
Institute for Intelligent Systems Research and Innovation (IISRI)（智能系统研究与创新研究所）；Deakin University（迪肯大学）；Harvard Paulson School of Engineering and Applied Sciences（哈佛保尔森工程与应用科学学院）；Harvard University（哈佛大学）
Maryam Zare, Parham M. Kebria, Abbas Khosravi, Saeid Nahavandi
备注：
This work has been submitted to the IEEE for possible publication
In recent years, the development of robotics and artificial intelligence (AI) systems has been nothing short of remarkable. As these systems continue to evolve, they are being utilized in increasingly complex and unstructured environments, such as autonomous driving, aerial robotics, and natural language processing. As a consequence, programming their behaviors manually or defining their behavior through reward functions (as done in reinforcement learning (RL)) has become exceedingly difficult. This is because such environments require a high degree of flexibility and adaptability, making it challenging to specify an optimal set of rules or reward signals that can account for all possible situations. In such environments, learning from an expert's behavior through imitation is often more appealing. This is where imitation learning (IL) comes into play - a process where desired behavior is learned by imitating an expert's behavior, which is provided through demonstrations.
This paper aims to provide an introduction to IL and an overview of its underlying assumptions and approaches. It also offers a detailed description of recent advances and emerging areas of research in the field. Additionally, the paper discusses how researchers have addressed common challenges associated with IL and provides potential directions for future research. Overall, the goal of the paper is to provide a comprehensive guide to the growing field of IL in robotics and AI.
Dexterous Manipulation through Imitation Learning: A Survey
Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System（天津智能集群技术与系统重点实验室）；School of Electrical and Information Engineering（电气与信息工程学院）；Shandong University（山东大学）；State Key Laboratory of General Artificial Intelligence（通用人工智能国家重点实验室）；Division of Robotics, Perception and Learning（机器人、感知与学习 division）；School of Control Science and Engineering（控制科学与工程学院）；Massachusetts Institute of Technology（麻省理工学院）；School of Computer Science（计算机科学学院）；State Key Laboratory of Multimodal Artificial Intelligence Systems（多模态人工智能系统国家重点实验室）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence（人工智能学院）
Learning from Observation: A Survey of Recent Advances
Department of Computer Science；Engineering Indian Institute of Technology, Madras Chennai Tamil Nadu India；National Institute of Technology, Karnataka Surathkal Karnataka India；Department of Data Science；AI Indian Institute of Technology Madras Chennai Tamil Nadu India；Wadhwani School of Data Science \& AI Chennai Tamil Nadu India；Indian Institute of Technology, Madras；National Institute of Technology, Karnataka；Indian Institute of Technology Madras；Wadhwani School of Data Science \& AI
Fusion Dynamical Systems with Machine Learning in Imitation Learning: A Comprehensive Overview
Multi-Scale Medical Robotics Centre, Ltd.（多尺度医疗机器人中心有限公司）；The Chinese University of Hong Kong（香港中文大学）；Technical University of Munich（慕尼黑工业大学）；Mondragon Unibertsitatea（蒙德拉贡大学）；Nantong University（南通大学）
Robotic Manipulation via Imitation Learning: Taxonomy, Evolution, Benchmark, and Challenges
Ecole Centrale de Lyon, CNRS, Universite Claude Bernard Lyon 1, INSA Lyon, Université Lumière Lyon 2, LIRIS, UMR5205（Lyon中央理工学院、CNRS、Claude Bernard Lyon 1大学、INSA Lyon、Lyon大学、LIRIS、UMR5205）；Beihang University（北航大学）；Dalian University of Technology（大连理工大学）；Institut Universitaire de France (IUF)（法国大学研究院（IUF））
Deep Reinforcement Learning for Autonomous Driving: A Survey
Navya（纳维亚）；Valeo（法雷奥）；ENSTA Paris, Institut Polytechnique de Paris（巴黎理工学院巴黎高等先进技术学院）；AKKA Technologies（AKKA科技公司）；National University of Ireland, Galway（爱尔兰国立高威大学）
Imitation Learning Policy based on Multi-Step Consistent Integration Shortcut Model
State Key Laboratory of Robotics and Systems, Harbin Institute of Technology（机器人与系统国家重点实验室，哈尔滨工业大学）；School of Computer Science and Engineering, Tianjin University of Technology（计算机科学与工程学院，天津理工大学）
Graduate School of Computer Science, Saarland University（萨尔大学计算机科学研究生院）；Bosch Center for Artificial Intelligence, Robert Bosch GmbH（罗伯特·博世有限公司博世人工智能中心）；CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全中心）
Imitrob: Imitation Learning Dataset for Training and Evaluating 6D Object Pose Estimators
Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague（布拉格捷克理工大学捷克信息学、机器人与控制论研究所）；Faculty of Mathematics, Physics and Informatics, Comenius University in Bratislava（布拉迪斯拉发夸美纽斯大学数学、物理与信息学院）
From Imitation to Exploration: End-to-end Autonomous Driving based on World Model
Department of Automation, Shanghai Jiao Tong University（自动化系，上海交通大学）；Key Laboratory of System Control and Information Processing, Ministry of Education of China（系统控制与信息处理重点实验室，中华人民共和国教育部）；Global Institute of Future Technology, Shanghai Jiao Tong University（未来技术全球研究院，上海交通大学）
Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing
School of Artificial Intelligence, Harbin Institute of Technology（哈尔滨工业大学人工智能学院）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）；Pengcheng Laboratory（鹏城实验室）；Suzhou Research Institute, Harbin Institute of Technology（哈尔滨工业大学苏州研究院）
DARIL: When Imitation Learning outperforms Reinforcement Learning in Surgical Action Planning
Surgical and Interventional Engineering, King's College London, London, UK（外科与介入工程，伦敦国王学院，伦敦，英国）；Interventional Engineering, King's College London, London, UK（介入工程，伦敦国王学院，伦敦，英国）
该研究在 CholecT50 上比较手术动作规划中的 IL 与 RL，提出 DARIL 基线，发现其显著优于三种 RL 方法，并揭示专家测试分布对 IL 的系统性偏好。
