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Imitation Learning (IL) is a powerful paradigm to teach robots to perform manipulation tasks by allowing them to learn from human demonstrations collected via teleoperation, but has mostly been limited to single-arm manipulation.
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Diederik Kingma and Max Welling · 2013
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“Multi-robot inverse reinforcement learning under occlusion with interactions”
Kenneth Bogert and Prashant Doshi · 2014
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“Towards learning hierarchical skills for multi-phase manipulation tasks”
Oliver Kroemer et al · 2015
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“Learning bimanual end-effector poses from demonstrations using task-parameterized dynamical systems”
Joao Silvério, Leonel Rozo, Sylvain Calinon and Darwin Caldwell · 2015
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“Learning to communicate with deep multi-agent reinforcement learning”
Jakob Foerster, Ioannis Assael, Nando De and Shimon Whiteson · 2016
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“Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation”
Tianhao Zhang et al · 2017
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“Learning attentional communication for multi-agent cooperation”
Jiechuan Jiang and Zongqing Lu · 2018
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“Shared-autonomy control for intuitive bimanual tele-manipulation”
Marco Laghi et al · 2018
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“RoboTurk: A Crowdsourcing Platform for Robotic Skill Learning through Imitation”
Ajay Mandlekar et al · 2018
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“Multi-agent generative adversarial imitation learning”
Jiaming Song, Hongyu Ren, Dorsa Sadigh and Stefano Ermon · 2018
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