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Machine learning techniques have enabled robots to learn narrow, yet complex tasks and also perform broad, yet simple skills with a wide variety of objects.
Task-oriented optimal grasping by multifingered robot hands
Zexiang Li and S Shankar Sastry · 1988
Earlier work this paper cites.
Alvinn: An autonomous land vehicle in a neural network
Dean A Pomerleau · 1989
Earlier work this paper cites.
Fetal grasping at 16 weeks’ gestation
D. Sherer · 1993
Earlier work this paper cites.
Mixture density networks
Christopher M Bishop · 1994
Earlier work this paper cites.
Robot grasp synthesis algorithms: A survey
Karun B Shimoga · 1996
Earlier work this paper cites.
Robot learning from demonstration
Christopher G Atkeson and Stefan Schaal · 1997
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Micro planning for mechanical assembly operations
SK Gupta, Christiaan JJ Paredis, and PF Brown · 1998
Earlier work this paper cites.
Cooperative manipulation of objects by multiple mobile robots with tools
Atsushi Yamashita, Jun Sasaki, Jun Ota, and Tamio Arai · 1998
Earlier work this paper cites.
A general framework for assembly planning: The motion space approach
Dan Halperin, J-C Latombe, and Randall H Wilson · 2000
Earlier work this paper cites.
Motion planning for humanoid robots
James Kuffner, Koichi Nishiwaki, Satoshi Kagami, Masayuki Inaba, and Hirochika Inoue · 2005
Earlier work this paper cites.
Behavior-grounded representation of tool affordances
Alexander Stoytchev · 2005
Earlier work this paper cites.
Grasp recognition in virtual reality for robot pregrasp planning by demonstration
Jacopo Aleotti and Stefano Caselli · 2006
Earlier work this paper cites.
Association of whole body motion from tool knowledge for humanoid robots
Dongheui Lee, Hirotoshi Kunori, and Yoshihiko Nakamura · 2008
Earlier work this paper cites.
A survey of robot learning from demonstration
Brenna D Argall, Sonia Chernova, Manuela Veloso, and Brett Browning · 2009
Earlier work this paper cites.
Robot motor skill coordination with em-based reinforcement learning
Petar Kormushev, Sylvain Calinon, and Darwin G Caldwell · 2010
Earlier work this paper cites.
Reinforcement learning to adjust robot movements to new situations
Jens Kober, Erhan Öztop, and Jan Peters · 2011
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A relational approach to tool-use learning in robots
Solly Brown and Claude Sammut · 2012
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Robot motion planning , volume 124
Jean-Claude Latombe · 2012
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Feedback motion planning and learning from demonstration in physical robotic assistance: differences and synergies
Martin Lawitzky, Jose Ramon Medina, Dongheui Lee, and Sandra Hirche · 2012
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Discovery of complex behaviors through contact-invariant optimization
Igor Mordatch, Emanuel Todorov, and Zoran Popović · 2012
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A probabilistic framework for task-oriented grasp stability assessment
Yasemin Bekiroglu, Dan Song, Lu Wang, and Danica Kragic · 2013
Se3-nets: Learning rigid body motion using deep neural networks
Arunkumar Byravan and Dieter Fox · 2017
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Self-supervised visual planning with temporal skip connections
Frederik Ebert, Chelsea Finn, Alex X Lee, and Sergey Levine · 2017
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Empirical evaluation of common contact models for planar impact
Nima Fazeli, Elliott Donlon, Evan Drumwright, and Alberto Rodriguez · 2017
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Deep visual foresight for planning robot motion
Chelsea Finn and Sergey Levine · 2017
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guided motion planning
Gu Ye and Ron Alterovitz · 2017
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Global search with bernoulli alternation kernel for task-oriented grasping informed by simulation
Rika Antonova, Mia Kokic, Johannes A Stork, and Danica Kragic · 2018
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Cross-entropy method
Dirk P Kroese, Reuven Y Rubinstein, Izack Cohen, Sergey Porotsky, and Thomas Taimre · 2013
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Exploring affordances and tool use on the icub
V Tikhanoff, U Pattacini, L Natale, and G Metta · 2013
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Scheduled sampling for sequence prediction with recurrent neural networks
Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer · 2015
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Deepmpc: Learning deep latent features for model predictive control
Ian Lenz, Ross A Knepper, and Ashutosh Saxena · 2015
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Embed to control: A locally linear latent dynamics model for control from raw images
Manuel Watter, Jost Springenberg, Joschka Boedecker, and Martin Riedmiller · 2015
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Convolutional lstm network: A machine learning approach for precipitation nowcasting
SHI Xingjian, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo · 2015
Cited alongside, same era.
End-to-end driving via conditional imitation learning
Felipe Codevilla, Matthias Miiller, Antonio López, Vladlen Koltun, and Alexey Dosovitskiy · 2018
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Learning task-oriented grasping for tool manipulation from simulated self-supervision
Kuan Fang, Yuke Zhu, Animesh Garg, Andrey Kurenkov, Viraj Mehta, Li Fei-Fei, and Silvio Savarese · 2018
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Stochastic adversarial video prediction
Alex X Lee, Richard Zhang, Frederik Ebert, Pieter Abbeel, Chelsea Finn, and Sergey Levine · 2018
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Overcoming exploration in reinforcement learning with demonstrations
Ashvin Nair, Bob McGrew, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel · 2018
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Zero-shot visual imitation
Deepak Pathak, Parsa Mahmoudieh, Guanghao Luo, Pulkit Agrawal, Dian Chen, Yide Shentu, Evan Shelhamer, Jitendra Malik, Alexei A Efros, and Trevor Darrell · 2018
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Chris Paxton, Yotam Barnoy, Kapil Katyal, Raman Arora, and Gregory D Hager · 2018
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Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov, and Sergey Levine · 2018
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Deep Imitative Models for Flexible Inference, Planning, and Control
N. Rhinehart, R. McAllister, and S. Levine · 2018
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Differentiable physics and stable modes for tool-use and manipulation planning
Marc Toussaint, Kelsey Allen, Kevin Smith, and Joshua B Tenenbaum · 2018
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Manipulation by feel: Touch-based control with deep predictive models
Stephen Tian, Frederik Ebert, Dinesh Jayaraman, Mayur Mudigonda, Chelsea Finn, Roberto Calandra, and Sergey Levine · 2019
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