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Training robotic policies in simulation suffers from the sim-to-real gap, as simulated dynamics can be different from real-world dynamics.
Identification of contact dynamics model parameters from constrained robotic operations
M Weber, K Patel, O Ma, and I Sharf · 2006
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Relative entropy policy search
Jan Peters, Katharina Mulling, and Yasemin Altun · 2010
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Agnostic system identification for model-based reinforcement learning
Stephane Ross and J Andrew Bagnell · 2012
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Experimental design in dynamical system identification: a bandit-based active learning approach
Artémis Llamosi, Adel Mezine, Florence d’Alché Buc, Véronique Letort, and Michèle Sebag · 2014
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Dynamic parameters identification of a humanoid robot using joint torque sensors and/or contact forces
Yusuke Ogawa, Gentiane Venture, and Christian Ott · 2014
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Probabilistic segmentation and targeted exploration of objects in cluttered environments
Herke Van Hoof, Oliver Kroemer, and Jan Peters · 2014
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Active articulation model estimation through interactive perception
Karol Hausman, Scott Niekum, Sarah Osentoski, and Gaurav S Sukhatme · 2015
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Physically consistent state estimation and system identification for contacts
Svetoslav Kolev and Emanuel Todorov · 2015
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Sim-to-real robot learning from pixels with progressive nets
Andrei A Rusu, Mel Vecerik, Thomas Rothörl, Nicolas Heess, Razvan Pascanu, and Raia Hadsell · 2016
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AprilTag 2: Efficient and robust fiducial detection
John Wang and Edwin Olson · 2016
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Opening a lockbox through physical exploration
Manuel Baum, Matthew Bernstein, Roberto Martin-Martin, Sebastian Höfer, Johannes Kulick, Marc Toussaint, Alex Kacelnik, and Oliver Brock · 2017
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Parameter and contact force estimation of planar rigid-bodies undergoing frictional contact
Nima Fazeli, Roman Kolbert, Russ Tedrake, and Alberto Rodriguez · 2017
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Combining learned and analytical models for predicting action effects
Alina Kloss, Stefan Schaal, and Jeannette Bohg · 2017
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Domain randomization for transferring deep neural networks from simulation to the real world
Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel · 2017
Cited alongside, same era.
Preparing for the unknown: Learning a universal policy with online system identification
Wenhao Yu, Jie Tan, C Karen Liu, and Greg Turk · 2017
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Augmenting physical simulators with stochastic neural networks: Case study of planar pushing and bouncing
Anurag Ajay, Jiajun Wu, Nima Fazeli, Maria Bauza, Leslie P Kaelbling, Joshua B Tenenbaum, and Alberto Rodriguez · 2018
Cited alongside, same era.
Using simulation and domain adaptation to improve efficiency of deep robotic grasping
Konstantinos Bousmalis, Alex Irpan, Paul Wohlhart, Yunfei Bai, Matthew Kelcey, Mrinal Kalakrishnan, Laura Downs, Julian Ibarz, Peter Pastor, Kurt Konolige, et al · 2018
Cited alongside, same era.
Physics-based selection of informative actions for interactive perception
Clemens Eppner, Roberto Martín-Martín, and Oliver Brock · 2018
Cited alongside, same era.
Closing the sim-to-real loop: Adapting simulation randomization with real world experience
Yevgen Chebotar, Ankur Handa, Viktor Makoviychuk, Miles Macklin, Jan Issac, Nathan Ratliff, and Dieter Fox · 2019
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Estimating mass distribution of articulated objects through physical interaction
Niranjan Kumar Kannabiran, Irfan Essa, and C Karen Liu · 2019
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A review of robot learning for manipulation: Challenges, representations, and algorithms
Oliver Kroemer, Scott Niekum, and George Konidaris · 2019
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Active domain randomization
Bhairav Mehta, Manfred Diaz, Florian Golemo, Christopher J Pal, and Liam Paull · 2019
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Learning domain randomization distributions for transfer of locomotion policies
Melissa Mozifian, Juan Camilo Gamboa Higuera, David Meger, and Gregory Dudek · 2019
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Iterative value-aware model learning
Amir-massoud Farahmand · 2018
Cited alongside, same era.
Sim-to-real transfer with neural-augmented robot simulation
Florian Golemo, Adrien Ali Taiga, Aaron Courville, and Pierre-Yves Oudeyer · 2018
Cited alongside, same era.
Gpu-accelerated robotic simulation for distributed reinforcement learning
Jacky Liang, Viktor Makoviychuk, Ankur Handa, Nuttapong Chentanez, Miles Macklin, and Dieter Fox · 2018
Cited alongside, same era.
Domain randomization for simulation-based policy optimization with transferability assessment
Fabio Muratore, Felix Treede, Michael Gienger, and Jan Peters · 2018
Cited alongside, same era.
Sim-to-real transfer of robotic control with dynamics randomization
Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel · 2018
Cited alongside, same era.
Sim-to-real: Learning agile locomotion for quadruped robots
Jie Tan, Tingnan Zhang, Erwin Coumans, Atil Iscen, Yunfei Bai, Danijar Hafner, Steven Bohez, and Vincent Vanhoucke · 2018
Cited alongside, same era.
Solving rubik’s cube with a robot hand
Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, Arthur Petron, Alex Paino, Matthias Plappert, Glenn Powell, Raphael Ribas, et al · 2019
Cited alongside, same era.
Deep Dynamics Models for Learning Dexterous Manipulation
Anusha Nagabandi, Kurt Konoglie, Sergey Levine, and Vikash Kumar · 2019
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Estimating mass distribution of articulated objects through non-prehensile manipulation
K Niranjan Kumar, Irfan Essa, and C Karen Liu · 2019
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Efficient off-policy meta-reinforcement learning via probabilistic context variables
Kate Rakelly, Aurick Zhou, Deirdre Quillen, Chelsea Finn, and Sergey Levine · 2019
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Bayessim: adaptive domain randomization via probabilistic inference for robotics simulators
Fabio Ramos, Rafael Carvalhaes Possas, and Dieter Fox · 2019
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Tossingbot: Learning to throw arbitrary objects with residual physics
Andy Zeng, Shuran Song, Johnny Lee, Alberto Rodriguez, and Thomas Funkhouser · 2019
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Environment probing interaction policies
Wenxuan Zhou, Lerrel Pinto, and Abhinav Gupta · 2019
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Context-aware dynamics model for generalization in model-based reinforcement learning
Kimin Lee, Younggyo Seo, Seunghyun Lee, Honglak Lee, and Jinwoo Shin · 2020
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