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Bridging the 'reality gap' that separates simulated robotics from experiments on hardware could accelerate robotic research through improved data availability.
Description and recognition of curved objects
Ramakant Nevatia and Thomas O Binford · 1977
Earlier work this paper cites.
Three-dimensional object recognition from single two-dimensional images
David G Lowe · 1987
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Real-time visual tracking of 3d objects with dynamic handling of occlusion
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Alvaro Collet, Dmitry Berenson, Siddhartha S Srinivasa, and Dave Ferguson · 2009
Earlier work this paper cites.
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Superhuman performance of surgical tasks by robots using iterative learning from human-guided demonstrations
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Alvaro Collet, Manuel Martinez, and Siddhartha S Srinivasa · 2011
Earlier work this paper cites.
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Brian Kulis, Kate Saenko, and Trevor Darrell · 2011
Earlier work this paper cites.
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Lixin Duan, Dong Xu, and Ivor Tsang · 2012
Earlier work this paper cites.
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Jie Tang, Stephen Miller, Arjun Singh, and Pieter Abbeel · 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.
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Revisiting batch normalization for practical domain adaptation
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How useful is photo-realistic rendering for visual learning?
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Berk Calli, Arjun Singh, Aaron Walsman, Siddhartha Srinivasa, Pieter Abbeel, and Aaron M Dollar · 2015
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Efficient reinforcement learning for robots using informative simulated priors
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Sim-to-real robot learning from pixels with progressive nets
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Adapting deep visuomotor representations with weak pairwise constraints
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Fetch and freight: Standard platforms for service robot applications
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