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Imitation learning is an approach in which an agent learns how to execute a task by trying to mimic how one or more teachers perform it.
Recent Advances in Imitation Learning from Observation
Faraz Torabi, Garrett Warnell, and Peter Stone. 2019b · 1905
Earlier work this paper cites.
Neuronlike adaptive elements that can solve difficult learning control problems
Andrew G Barto, Richard S Sutton, and Charles W Anderson. 1983 · 1983
Earlier work this paper cites.
ALVINN: An Autonomous Land Vehicle in a Neural Network. In International Conference on Neural Information Processing Systems . 305–313
Dean A. Pomerleau. 1988 · 1988
Earlier work this paper cites.
Efficient memory-based learning for robot control
Andrew William Moore. 1990 · 1990
Earlier work this paper cites.
A Framework for Behavioural Cloning. In Machine Intelligence 15, Intelligent Agents [St. Catherine’s College, Oxford, July 1995] . Oxford University, GBR, 103–129
Michael Bain and Claude Sammut. 1999 · 1995
Earlier work this paper cites.
Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding. In Advances in Neural Information Processing Systems 8, NIPS, Denver, CO, USA, November 27-30, 1995 , David S. Touretzky, Michael Mozer, and Michael E. Hasselmo (Eds.). MIT Press, 1038–1044
Richard S. Sutton. 1995 · 1995
Earlier work this paper cites.
RoboCup: A Challenge Problem for AI
Hiroaki Kitano, Minoru Asada, Yasuo Kuniyoshi, Itsuki Noda, Eiichi Osawa, and Hitoshi Matsubara. 1997 · 1997
Earlier work this paper cites.
Reinforcement Learning Using Neural Networks, with Applications to Motor Control. (Apprentissage par renforcement utilisant des réseaux de neurones, avec des applications au contrôle moteur)
Rémi Coulom. 2002 · 2002
Earlier work this paper cites.
Computational approaches to motor learning by imitation
Stefan Schaal, Auke Ijspeert, and Aude Billard. 2003 · 2003
Earlier work this paper cites.
Juarez Monteiro, Nathan Gavenski, Roger Granada, Felipe Meneguzzi, and Rodrigo C. Barros. 2020 · 2004
Earlier work this paper cites.
SuperTuxKart
Joerg Henrichs. 2006 · 2006
Earlier work this paper cites.
A Comprehensive Survey of Multiagent Reinforcement Learning
Lucian Busoniu, Robert Babuska, and Bart De Schutter. 2008 · 2007
Earlier work this paper cites.
What is the teacher’s role in robot programming by demonstration?
Sylvain Calinon and Aude G. Billard. 2007 · 2007
Earlier work this paper cites.
Matteo Lucchi, Friedemann Zindler, Stephan Mühlbacher-Karrer, and Horst Pichler. 2007 · 2007
Earlier work this paper cites.
Teaching collaborative multi-robot tasks through demonstration. In Humanoids 2008 - 8th IEEE-RAS International Conference on Humanoid Robots . IEEE
S. Chernova and M. Veloso. 2008 · 2008
Earlier work this paper cites.
Visualizing Data using t-SNE
Laurens van der Maaten and Geoffrey Hinton. 2008 · 2008
Earlier work this paper cites.
Maximum entropy inverse reinforcement learning.. In Aaai , Vol. 8. Chicago, IL, USA, 1433–1438
Brian D Ziebart, Andrew L Maas, J Andrew Bagnell, Anind K Dey, et al · 2008
Earlier work this paper cites.
A Survey on Transfer Learning
Sinno Jialin Pan and Qiang Yang. 2010 · 2009
Earlier work this paper cites.
A Cat-Like Robot Real-Time Learning to Run. In Adaptive and Natural Computing Algorithms, 9th International Conference, ICANNGA 2009, Kuopio, Finland, April 23-25, 2009, Revised Selected Papers (Lecture Notes in Computer Science, Vol. 5495) , Mikko Kolehmainen, Pekka J. Toivanen, and Bartlomiej Beliczynski (Eds.). Springer, 380–390
Pawel Wawrzynski. 2009 · 2009
Earlier work this paper cites.
Infinite-Horizon Model Predictive Control for Periodic Tasks with Contacts. In Robotics: Science and Systems VII, University of Southern California, Los Angeles, CA, USA, June 27-30, 2011 , Hugh F. Durrant-Whyte, Nicholas Roy, and Pieter Abbeel (Eds.)
Tom Erez, Yuval Tassa, and Emanuel Todorov. 2011 · 2011
Earlier work this paper cites.
A reduction of imitation learning and structured prediction to no-regret online learning. In Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS) . 627–635
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell. 2011 · 2011
Earlier work this paper cites.
Imitating human playing styles in Super Mario Bros
Juan Ortega, Noor Shaker, Julian Togelius, and Georgios N. Yannakakis. 2013 · 2012
Earlier work this paper cites.
Teaching coordinated strategies to soccer robots via imitation. In 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO) . IEEE
Saleha Raza, Sajjad Haider, and Mary-Anne Williams. 2012a · 2012
Earlier work this paper cites.
Teaching Coordinated Strategies to Soccer Robots via Imitation. In International Conference on Robotics and Biomimetics (ROBIO 2012) . 1434–1439
Saleha Raza, Sajjad Haider, and Mary-Anne Williams. 2012b · 2012
Earlier work this paper cites.
Synthesis and stabilization of complex behaviors through online trajectory optimization. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2012, Vilamoura, Algarve, Portugal, October 7-12, 2012 . IEEE, 4906–4913
Yuval Tassa, Tom Erez, and Emanuel Todorov. 2012 · 2012
Earlier work this paper cites.
MuJoCo: A physics engine for model-based control. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE
Emanuel Todorov, Tom Erez, and Yuval Tassa. 2012 · 2012
Earlier work this paper cites.
The Arcade Learning Environment: An Evaluation Platform for General Agents
M. G. Bellemare, Y. Naddaf, J. Veness, and M. Bowling. 2013 · 2013
Earlier work this paper cites.
Scalable multiagent learning through indirect encoding of policy geometry
David B. D’Ambrosio and Kenneth O. Stanley. 2013 · 2013
Earlier work this paper cites.
Generative Adversarial Nets. In Advances in Neural Information Processing Systems , Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K.Q. Weinberger (Eds.), Vol. 27. Curran Associates, Inc
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Learning a Super Mario controller from examples of human play. In 2014 IEEE Congress on Evolutionary Computation (CEC) . IEEE
Geoffrey Lee, Min Luo, Fabio Zambetta, and Xiaodong Li. 2014 · 2014
Earlier work this paper cites.
Guidelines for snowballing in systematic literature studies and a replication in software engineering. In Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering (London, England, United Kingdom) (EASE ’14) . Association for Computing Machinery, New York, NY, USA, Article 38, 10 pages
Claes Wohlin. 2014 · 2014
Earlier work this paper cites.
TORCS, The Open Racing Car Simulator
Bernhard Wymann, Eric Espié, Christophe Guionneau, Christos Dimitrakakis, Rémi Coulom, and Andrew Sumner. 2014 · 2014
Earlier work this paper cites.
The Pascal Visual Object Classes Challenge: A Retrospective
M. Everingham, S. M. A. Eslami, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. 2015 · 2015
Earlier work this paper cites.
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei. 2015 · 2015
Earlier work this paper cites.
High-dimensional continuous control using generalized advantage estimation
John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel. 2015 · 2015
Earlier work this paper cites.
Maze Gym
Pierre Aumjaud. 2016 · 2016
Earlier work this paper cites.
Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization. In Proceedings of The 33rd International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 48) , Maria Florina Balcan and Kilian Q. Weinberger (Eds.). PMLR, New York, New York, USA, 49–58
Chelsea Finn, Sergey Levine, and Pieter Abbeel. 2016 · 2016
Earlier work this paper cites.
Generative adversarial imitation learning. In Advances in neural information processing systems . Advances in neural information processing systems, 4565–4573
Jonathan Ho and Stefano Ermon. 2016 · 2016
Earlier work this paper cites.
The Malmo Platform for Artificial Intelligence Experimentation. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9-15 July 2016 , Subbarao Kambhampati (Ed.). IJCAI/AAAI Press, 4246–4247
Matthew Johnson, Katja Hofmann, Tim Hutton, and David Bignell. 2016 · 2016
Earlier work this paper cites.
ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning. In IEEE Conference on Computational Intelligence and Games . IEEE, Santorini, Greece, 341–348
Michał Kempka, Marek Wydmuch, Grzegorz Runc, Jakub Toczek, and Wojciech Jaśkowski. 2016 · 2016
Earlier work this paper cites.
How to Use t-SNE Effectively
Martin Wattenberg, Fernanda Viégas, and Ian Johnson. 2016 · 2016
Earlier work this paper cites.
Deep Reinforcement Learning: A Brief Survey
Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, and Anil Anthony Bharath. 2017 · 2017
Cited alongside, same era.
End-to-End Differentiable Adversarial Imitation Learning. In Proceedings of the 34th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 70) , Doina Precup and Yee Whye Teh (Eds.). PMLR, 390–399
Nir Baram, Oron Anschel, Itai Caspi, and Shie Mannor. 2017 · 2017
Cited alongside, same era.
Deep Reinforcement Learning from Human Preferences. In Advances in Neural Information Processing Systems , I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. 2017 · 2017
Cited alongside, same era.
Learning Transferable Policies for Monocular Reactive MAV Control
Shreyansh Daftry, J. Andrew Bagnell, and Martial Hebert. 2017 · 2017
Cited alongside, same era.
Learning by Cheating. In Proceedings of the Conference on Robot Learning (Proceedings of Machine Learning Research, Vol. 100) , Leslie Pack Kaelbling, Danica Kragic, and Komei Sugiura (Eds.). PMLR, 66–75
Dian Chen, Brady Zhou, Vladlen Koltun, and Philipp Krähenbühl. 2020 · 2020
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A survey on multi-agent deep reinforcement learning: from the perspective of challenges and applications
Wei Du and Shifei Ding. 2020 · 2020
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Imitating Unknown Policies via Exploration. In International British Machine Vision Virtual Conference . 1–8
Nathan Gavenski, Juarez Monteiro, Roger Granada, Felipe Meneguzzi, and Rodrigo C. Barros. 2020 · 2020
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Learning Latent Plans from Play. In Proceedings of the Conference on Robot Learning (Proceedings of Machine Learning Research, Vol. 100) , Leslie Pack Kaelbling, Danica Kragic, and Komei Sugiura (Eds.). PMLR, 1113–1132
Corey Lynch, Mohi Khansari, Ted Xiao, Vikash Kumar, Jonathan Tompson, Sergey Levine, and Pierre Sermanet. 2020 · 2020
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CARLA: An Open Urban Driving Simulator. In Proceedings of the 1st Annual Conference on Robot Learning . 1–16
Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun. 2017 · 2017
Cited alongside, same era.
One-Shot Imitation Learning. In Advances in Neural Information Processing Systems , I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc
Yan Duan, Marcin Andrychowicz, Bradly Stadie, OpenAI Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, and Wojciech Zaremba. 2017 · 2017
Cited alongside, same era.
Imitation learning: A survey of learning methods
Ahmed Hussein, Mohamed Medhat Gaber, Eyad Elyan, and Chrisina Jayne. 2017 · 2017
Cited alongside, same era.
InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations. In Advances in Neural Information Processing Systems , I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc
Yunzhu Li, Jiaming Song, and Stefano Ermon. 2017 · 2017
Cited alongside, same era.
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, Pieter Abbeel, and Igor Mordatch. 2017 · 2017
Cited alongside, same era.
Methods for interpreting and understanding deep neural networks
Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller. 2018 · 2017
Cited alongside, same era.
Emergence of Grounded Compositional Language in Multi-Agent Populations
Igor Mordatch and Pieter Abbeel. 2017 · 2017
Cited alongside, same era.
Combining self-supervised learning and imitation for vision-based rope manipulation. In 2017 IEEE International Conference on Robotics and Automation (ICRA) . IEEE
Ashvin Nair, Dian Chen, Pulkit Agrawal, Phillip Isola, Pieter Abbeel, Jitendra Malik, and Sergey Levine. 2017 · 2017
Cited alongside, same era.
RIDM: Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstration
Brahma S. Pavse, Faraz Torabi, Josiah Hanna, Garrett Warnell, and Peter Stone. 2020 · 2020
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Artificial Intelligence: A Modern Approach (4th Edition)
Stuart Russell and Peter Norvig. 2020 · 2020
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Artificial Intelligence: A Modern Approach
S.J. Russell, S. Russell, and P. Norvig. 2020 · 2020
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dm_control: Software and tasks for continuous control
Saran Tunyasuvunakool, Alistair Muldal, Yotam Doron, Siqi Liu, Steven Bohez, Josh Merel, Tom Erez, Timothy Lillicrap, Nicolas Heess, and Yuval Tassa. 2020 · 2020
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Intrinsic Reward Driven Imitation Learning via Generative Model. In Proceedings of the 37th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 119) , Hal Daumé III and Aarti Singh (Eds.). PMLR, 10925–10935
Xingrui Yu, Yueming Lyu, and Ivor Tsang. 2020 · 2020
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Off-Policy Imitation Learning from Observations. In Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 12402–12413
Zhuangdi Zhu, Kaixiang Lin, Bo Dai, and Jiayu Zhou. 2020 · 2020
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A Comprehensive Survey on Transfer Learning
Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, and Qing He. 2021 · 2020
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Inverse dynamics vs. forward dynamics in direct transcription formulations for trajectory optimization. In 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 12752–12758
Henrique Ferrolho, Vladimir Ivan, Wolfgang Merkt, Ioannis Havoutis, and Sethu Vijayakumar. 2021 · 2021
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panda-gym: Open-source goal-conditioned environments for robotic learning
Quentin Gallouédec, Nicolas Cazin, Emmanuel Dellandréa, and Liming Chen. 2021 · 2021
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Imitation Learning from Observations under Transition Model Disparity. In Deep RL Workshop NeurIPS 2021
Tanmay Gangwani, Yuan Zhou, and Jian Peng. 2021 · 2021
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Self-supervised imitation learning from observation
Nathan Schneider Gavenski. 2021 · 2021
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MobILE: Model-Based Imitation Learning From Observation Alone. In Advances in Neural Information Processing Systems , M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan (Eds.), Vol. 34. Curran Associates, Inc., 28598–28611
Rahul Kidambi, Jonathan Chang, and Wen Sun. 2021 · 2021
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PILOT: Efficient Planning by Imitation Learning and Optimisation for Safe Autonomous Driving. In IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021, Prague, Czech Republic, September 27 - Oct. 1, 2021 . IEEE, 1442–1449
Henry Pulver, Francisco Eiras, Ludovico Carozza, Majd Hawasly, Stefano V. Albrecht, and Subramanian Ramamoorthy. 2021 · 2021
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Cross-domain Imitation from Observations. In Proceedings of the 38th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 139) , Marina Meila and Tong Zhang (Eds.). PMLR, 8902–8912
Dripta S. Raychaudhuri, Sujoy Paul, Jeroen Vanbaar, and Amit K. Roy-Chowdhury. 2021 · 2021
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Self-Supervised Disentangled Representation Learning for Third-Person Imitation Learning. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE
Jinghuan Shang and Michael S. Ryoo. 2021 · 2021
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DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE
Faraz Torabi, Garrett Warnell, and Peter Stone. 2021 · 2021
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Imitation learning: Progress, taxonomies and opportunities
Boyuan Zheng, Sunny Verma, Jianlong Zhou, Ivor Tsang, and Fang Chen. 2021 · 2021
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IL-flOw: Imitation Learning from Observation using Normalizing Flows
Wei-Di Chang, Juan Camilo Gamboa Higuera, Scott Fujimoto, David Meger, and Gregory Dudek. 2022 · 2022
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MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge. In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track
Linxi Fan, Guanzhi Wang, Yunfan Jiang, Ajay Mandlekar, Yuncong Yang, Haoyi Zhu, Andrew Tang, De-An Huang, Yuke Zhu, and Anima Anandkumar. 2022 · 2022
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How Resilient Are Imitation Learning Methods to Sub-optimal Experts?. In Intelligent Systems , João Carlos Xavier-Junior and Ricardo Araújo Rios (Eds.). Springer International Publishing, Cham, 449–463
Nathan Gavenski, Juarez Monteiro, Adilson Medronha, and Rodrigo C. Barros. 2022 · 2022
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More Sanity Checks for Saliency Maps. In Foundations of Intelligent Systems - 26th International Symposium, ISMIS 2022, Cosenza, Italy, October 3-5, 2022, Proceedings (Lecture Notes in Computer Science, Vol. 13515) , Michelangelo Ceci, Sergio Flesca, Elio Masciari, Giuseppe Manco, and Zbigniew W. Ras (Eds.). Springer, 175–184
Lars Holmberg, Carl Johan Helgstrand, and Niklas Hultin. 2022 · 2022
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Improved Policy Optimization for Online Imitation Learning. In Proceedings of The 1st Conference on Lifelong Learning Agents (Proceedings of Machine Learning Research, Vol. 199) , Sarath Chandar, Razvan Pascanu, and Doina Precup (Eds.). PMLR, 1146–1173
Jonathan Wilder Lavington, Sharan Vaswani, and Mark Schmidt. 2022 · 2022
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CACTI: A Framework for Scalable Multi-Task Multi-Scene Visual Imitation Learning
Zhao Mandi, Homanga Bharadhwaj, Vincent Moens, Shuran Song, Aravind Rajeswaran, and Vikash Kumar. 2022 · 2022
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Conditional Imitation Learning for Multi-Agent Games
Andy Shih, Stefano Ermon, and Dorsa Sadigh. 2022 · 2022
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Planning for Sample Efficient Imitation Learning. In Advances in Neural Information Processing Systems , Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho (Eds.)
Zhao-Heng Yin, Weirui Ye, Qifeng Chen, and Yang Gao. 2022 · 2022
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Data Quality in Imitation Learning
Suneel Belkhale, Yuchen Cui, and Dorsa Sadigh. 2023 · 2023
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A Safe Driving Decision-making Methodology based on Cascade Imitation Learning Network for Automated Commercial Vehicles
Weiming Hu, Xu Li, Jinchao Hu, Dong Kong, Yue Hu, Qimin Xu, Yan Liu, Xiang Song, and Xuan Dong. 2023 · 2023
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Repairing Deep Neural Networks Based on Behavior Imitation
Zhen Liang, Taoran Wu, Changyuan Zhao, Wanwei Liu, Bai Xue, Wenjing Yang, and Ji Wang. 2023 · 2023
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Xingzhou Lou, Jiaxian Guo, Junge Zhang, Jun Wang, Kaiqi Huang, and Yali Du. 2023 · 2023
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On the recognition of human activities and the evaluation of its imitation by robotic systems
Raphael Memmesheimer. 2023 · 2023
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Self-Supervised Adversarial Imitation Learning
Juarez Monteiro, Nathan Gavenski, Felipe Meneguzzi, and Rodrigo C. Barros. 2023 · 2023
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Dynamical System-based Imitation Learning for Visual Servoing using the Large Projection Formulation. In ICRA’23-IEEE Int. Conf. on Robotics and Automation
Antonio Paolillo, Paolo Robuffo Giordano, and Matteo Saveriano. 2023 · 2023
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Stable Motion Primitives via Imitation and Contrastive Learning
Rodrigo Pérez-Dattari and Jens Kober. 2023 · 2023
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Gymnasium
Mark Towers, Jordan K Terry, Ariel Kwiatkowski, John U. Balis, Gianluca De Cola, Tristan Deleu, Manuel Goulão, Andreas Kallinteris, Arjun KG, Markus Krimmel, Rodrigo Perez-Vicente, Andrea Pierré, Sander Schulhoff, Jun Jet Tai, Andrew Tan Jin Shen, and Omar G. Younis. 2023 · 2023
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Data Driven Reward Initialization for Preference based Reinforcement Learning
Mudit Verma and Subbarao Kambhampati. 2023 · 2023
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Watch and Act: Learning Robotic Manipulation From Visual Demonstration
Shuo Yang, Wei Zhang, Ran Song, Jiyu Cheng, Hesheng Wang, and Yibin Li. 2023 · 2023
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