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We present a learning-based force-torque dynamics to achieve model-based control for contact-rich peg-in-hole task using force-only inputs.
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Hong Qiao, BS Dalay, and RM Parkin · 1995
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Hybrid control approach to the peg-in hole problem
Yangmin Li · 1997
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Lorenzo M Brignone and Martin Howarth · 2002
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An assembly process modeling and analysis for robotic multiple peg-in-hole
Yanqiong Fei and Xifang Zhao · 2003
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Tine Lefebvre, Herman Bruyninckx, and Joris De Schutter · 2005
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Bayesian hybrid model-state estimation applied to simultaneous contact formation recognition and geometrical parameter estimation
Klaas Gadeyne, Tine Lefebvre, and Herman Bruyninckx · 2005
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Tine Lefebvre, Jing Xiao, Herman Bruyninckx, and Gudrun De Gersem · 2005
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Pieter-Tjerk De Boer, Dirk P Kroese, Shie Mannor, and Reuven Y Rubinstein · 2005
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Dynamic analysis for peg-in-hole assembly with contact deformation
Yanchun Xia, Yuehong Yin, and Zhaoneng Chen · 2006
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Using model knowledge for learning inverse dynamics
Duy Nguyen-Tuong and Jan Peters · 2010
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Marc Deisenroth and Carl E Rasmussen · 2011
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Contact states recognition in robotic part mating based on support vector machines
Zivana Jakovljevic, Petar B Petrovic, and Janko Hodolic · 2012
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Learning neural network policies with guided policy search under unknown dynamics
Sergey Levine and Pieter Abbeel · 2014
Teach industrial robots peg-hole-insertion by human demonstration
Te Tang, Hsien-Chung Lin, Yu Zhao, Yongxiang Fan, Wenjie Chen, and Masayoshi Tomizuka · 2016
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Herke Van Hoof, Nutan Chen, Maximilian Karl, Patrick van der Smagt, and Jan Peters · 2016
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Guided policy search via approximate mirror descent
William H Montgomery and Sergey Levine · 2016
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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Deep reinforcement learning for high precision assembly tasks
T. Inoue, G. De Magistris, A. Munawar, T. Yokoya, and R. Tachibana · 2017
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Force/torque data modeling for contact position estimation in peg-in-hole assembling application
Mustafa Waad Abdullah, Hubert Roth, Michael Weyrich, Jürgen Wahrburg, and Padipat Pluemworasawat · 2015
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Learning contact-rich manipulation skills with guided policy search
Sergey Levine, Nolan Wagener, and Pieter Abbeel · 2015
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A learning-based framework for robot peg-hole-insertion
Te Tang, Hsien-Chung Lin, and Masayoshi Tomizuka · 2015
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Efficient reinforcement learning for robots using informative simulated priors
Mark Cutler and Jonathan P How · 2015
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End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
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One-shot learning of manipulation skills with online dynamics adaptation and neural network priors
Justin Fu, Sergey Levine, and Pieter Abbeel · 2016
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Optimal path planning and control of assembly robots for hard-measuring easy-deformation assemblies
An Wan, Jing Xu, Heping Chen, Song Zhang, and Ken Chen · 2017
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Learning to represent haptic feedback for partially-observable tasks
Jaeyong Sung, J Kenneth Salisbury, and Ashutosh Saxena · 2017
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Survey of model-based reinforcement learning: Applications on robotics
Athanasios S Polydoros and Lazaros Nalpantidis · 2017
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Realtime state estimation with tactile and visual sensing for inserting a suction-held object
Kuan-Ting Yu and Alberto Rodriguez · 2018
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Michelle A Lee, Yuke Zhu, Krishnan Srinivasan, Parth Shah, Silvio Savarese, Li Fei-Fei, Animesh Garg, and Jeannette Bohg · 2018
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The learning-based optimization algorithm for robotic dual peg-in-hole assembly
Zhimin Hou, Markus Philipp, Kuangen Zhang, Yong Guan, Ken Chen, and Jing Xu · 2018
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Feedback deep deterministic policy gradient with fuzzy reward for robotic multiple peg-in-hole assembly tasks
Jing Xu, Zhimin Hou, Wei Wang, Bohao Xu, Kuangen Zhang, and Ken Chen · 2018
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Manipulation by feel: Touch-based control with deep predictive models
S. Tian, F. Ebert, D. Jayaraman, M. Mudigonda, C. Finn, R. Calandra, and S. Levine · 2019
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