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Insertion is a challenging haptic and visual control problem with significant practical value for manufacturing.
“Learning reactive admittance control”
Vijaykumar Gullapalli, Roderic Grupen and Andrew Barto · 1992
Earlier work this paper cites.
“Learning admittance mappings for force-guided assembly”
Vijaykumar Gullapalli, Andrew Barto and Roderic Grupen · 1994
Earlier work this paper cites.
“Peg-in-hole assembly based on two-phase scheme and F/T sensor for dual-arm robot”
Xianmin Zhang, Yanglong Zheng, Jun Ota and Yanjiang Huang · 2004
Earlier work this paper cites.
“Robot motion planning”
Jean-Claude Latombe · 2012
Earlier work this paper cites.
“MuJoCo: A physics engine for model-based control”
Emanuel Todorov, Tom Erez and Yuval Tassa · 2012
Earlier work this paper cites.
“Auto-Encoding Variational Bayes”
Diederik. Kingma and Max Welling · 2013
Earlier work this paper cites.
“Task transfer via collaborative manipulation for insertion assembly”
Klas Kronander, Etienne Burdet and Aude Billard · 2014
Earlier work this paper cites.
“How transferable are features in deep neural networks?”
Jason Yosinski, Jeff Clune, Yoshua Bengio and Hod Lipson · 2014
Earlier work this paper cites.
“Continuous control with deep reinforcement learning”
Timothy Lillicrap et al · 2015
Earlier work this paper cites.
“Human-level control through deep reinforcement learning”
V. Mnih et al · 2015
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“Deep spatial autoencoders for visuomotor learning”
Chelsea Finn et al · 2016
Cited alongside, same era.
“End-to-end training of deep visuomotor policies”
Sergey Levine, Chelsea Finn, Trevor Darrell and Pieter Abbeel · 2016
Cited alongside, same era.
“Sim-to-real robot learning from pixels with progressive nets”
Andrei Rusu et al · 2016
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“Softtarget regularization: An effective technique to reduce over-fitting in neural networks”
Armen Aghajanyan · 2017
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“A Distributional Perspective on Reinforcement Learning”
Marc. Bellemare, Will Dabney and R“’emi Munos · 2017
“Pves: Position-velocity encoders for unsupervised learning of structured state representations”
Rico Jonschkowski, Roland Hafner, Jonathan Scholz and Martin Riedmiller · 2017
Later among the works it cites.
“Overcoming exploration in reinforcement learning with demonstrations”
Ashvin Nair et al · 2017
Later among the works it cites.
“Data-efficient deep reinforcement learning for dexterous manipulation”
Ivaylo Popov et al · 2017
Later among the works it cites.
“Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards”
Matej Vecer“’k et al · 2017
Later among the works it cites.
“Deep Q-learning From Demonstrations”
Todd Hester et al · 2018
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Cited alongside, same era.
“Unsupervised pixel-level domain adaptation with generative adversarial networks”
Konstantinos Bousmalis et al · 2017
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“Google Vizier: A Service for Black-Box Optimization”
Daniel Golovin et al · 2017
Cited alongside, same era.
“Deep reinforcement learning for high precision assembly tasks”
Tadanobu Inoue et al · 2017
Cited alongside, same era.
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“A Framework for Robot Manipulation: Skill Formalism, Meta Learning and Adaptive Control”
Lars Johannsmeier, Malkin Gerchow and Sami Haddadin · 2018
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A Mahmood, Dmytro Korenkevych, Brent Komer and James Bergstra · 2018
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