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In this work, we propose a novel robot learning framework called Neural Task Programming (NTP), which bridges the idea of few-shot learning from demonstration and neural program induction.
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“A Survey of Robot Learning from Demonstration”
Brenna Argall, Sonia Chernova, Manuela Veloso and Brett Browning · 2009
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“Towards one shot learning by imitation for humanoid robots”
Yan Wu and Yiannis Demiris · 2010
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“A reduction of imitation learning and structured prediction to no-regret online learning”
Stéphane Ross, Geoffrey Gordon and Drew Bagnell · 2011
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“Neural programmer-interpreters”
Scott Reed and Nando de Freitas · 2016
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“Autonomous Multiple-Throw Multilateral Surgical Suturing with a Mechanical Needle Guide and Optimization based Needle Planning”
Siddarth Sen* et al · 2016
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“Matching networks for one shot learning”
Oriol Vinyals, Charles Blundell, Tim Lillicrap and Daan Wierstra · 2016
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“Modular Multitask Reinforcement Learning with Policy Sketches”
Jacob Andreas, Dan Klein and Sergey Levine · 2017
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Jonathon Cai, Richard Shin and Dawn Song · 2017
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“pybullet, a Python module for physics simulation, games, robotics and machine learning”, http://pybullet.org/ , 2016–2017
Erwin Coumans and Yunfei Bai · 2017
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