Imitation learning: a suvey of learning methods.
Date
2017-04-11Author
Hussein, Ahmed
Gaber, Mohamed Medhat
Elyan, Eyad
Jayne, Chrisina
Metadata
Show full item recordCitation
HUSSEIN, A., GABER, M.M., ELYAN, E. and JAYNE, C. 2017 Imitation learning: a survey of learning methods. ACM computing surveys [online], 50(2), article 21. Available from: https://doi.org/10.1145/3054912
Abstract
Imitation learning techniques aim to mimic human behavior in a given task. An agent (a learning machine) is trained to perform a task from demonstrations by learning a mapping between observations and actions. The idea of teaching by imitation has been around for many years, however, the field is gaining attention recently due to advances in computing and sensing as well as rising demand for intelligent applications. The paradigm of learning by imitation is gaining popularity because it facilitates teaching complex tasks with minimal expert knowledge of the tasks. Generic imitation learning methods could potentially reduce the problem of teaching a task to that of providing demonstrations; without the need for explicit programming or designing reward functions specific to the task. Modern sensors are able to collect and transmit high volumes of data rapidly, and processors with high computational power allow fast processing that maps the sensory data to actions in a timely manner. This opens the door for many potential AI applications that require real-time perception and reaction such as humanoid robots, self-driving vehicles, human computer interaction and computer games to name a few. However, specialized algorithms are needed to effectively and robustly learn models as learning by imitation poses its own set of challenges. In this paper, we survey imitation learning methods and present design options in different steps of the learning process. We introduce a background and motivation for the field as well as highlight challenges specific to the imitation problem. Methods for designing and evaluating imitation learning tasks are categorized and reviewed. Special attention is given to learning methods in robotics and games as these domains are the most popular in the literature and provide a wide array of problems and methodologies. We extensively discuss combining imitation learning approaches using different sources and methods, as well as incorporating other motion learning methods to enhance imitation. We also discuss the potential impact on industry, present major applications and highlight current and future research directions.
Publisher link
https://doi.org/10.1145/3054912Permalink for this record
http://hdl.handle.net/10059/2298Collections in which this item appears
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc/4.0
Related items
Showing items related by title, author, creator and subject.
-
Journals and jottings on entrepreneurial learning journeys.
Fulford, Heather; Bailey, Moira (Academic Conferences and Publishing International Limited http://www.proceedings.com/24280.html, 2014-09-18)FULFORD, H. and BAILEY, M. 2014. Journals and jottings on entrepreneurial learning journeys. In Galbraith, B. (ed.) Proceedings of the 9th European conference on innovation and entrepreneurship (ECIE 2014), 18-19 September 2014, Belfast, UK. Reading: Academic Conferences Ltd [online], pages 198-206. Available from: http://www.proceedings.com/24280.htmlReview of relevant literature highlighted that entrepreneurs need help to reflect on, and make sense of, the challenges and opportunities that occur during the entrepreneurial process. For students who are unfamiliar with ... -
Deep imitation learning for 3D navigation tasks.
Hussein, Ahmed; Elyan, Eyad; Gaber, Mohamed Medhat; Jayne, Chrisina (Springer https://doi.org/10.1007/s00521-017-3241-z, 2017-12-04)HUSSEIN, A., ELYAN, E., GABER, M.M. and JAYNE, C. 2018. Deep imitation learning for 3D navigation tasks. Neural computing and applications [online], 29(7), pages 389-404. Available from: https://doi.org/10.1007/s00521-017-3241-zDeep learning techniques have shown success in learning from raw high dimensional data in various applications. While deep reinforcement learning is recently gaining popularity as a method to train intelligent agents, ... -
Informed pair selection for self-paced metric learning in Siamese neural networks.
Martin, Kyle; Wiratunga, Nirmalie; Massie, Stewart; Clos, Jérémie (Springer https://doi.org/10.1007/978-3-030-04191-5_3, 2018-11-16)MARTIN, K., WIRATUNGA, N., MASSIE, S. and CLOS, J. 2018. Informed pair selection for self-paced metric learning in Siamese neural networks. In Bramer, M. and PETRIDIS, M. (eds.) Artificial intelligence xxxv: proceedings of the 38th British Computer Society's specialist group on artificial intelligence (SGAI) annual international artificial intelligence conference (AI-2018), 11-13 December 2018, Cambridge, UK. Lecture notes in artificial intelligence, 11311. Cham: Springer [online], pages 34-49. Available from: https://doi.org/10.1007/978-3-030-04191-5_3Siamese Neural Networks (SNNs) are deep metric learners that use paired instance comparisons to learn similarity. The neural feature maps learnt in this way provide useful representations for classification tasks. Learning ...