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We address a class of manipulation problems where the robot perceives the scene with a depth sensor and can move its end effector in a space with six degrees of freedom -- 3D position and orientation.
Motion planning and the design of orienting devices for vibratory part feeders
T. Lozano-Pérez · 1986
Earlier work this paper cites.
Learning to perceive and act by trial and error
S. Whitehead and D. Ballard · 1991
Earlier work this paper cites.
On-line Q-learning using connectionist systems
G. Rummery and M. Niranjan · 1994
Earlier work this paper cites.
On-line policy improvement using Monte-Carlo search
G. Tesauro and G. Galperin · 1997
Earlier work this paper cites.
Eye movements for reward maximization
N. Sprague and D. Ballard · 2004
Earlier work this paper cites.
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Earlier work this paper cites.
Automated Construction of Robotic Manipulation Programs
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Earlier work this paper cites.
Active vision in robotic systems: A survey of recent developments
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Earlier work this paper cites.
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I. Lenz, H. Lee, and A. Saxena · 2015
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A. ten Pas, M. Gualtieri, K. Saenko, and R. Platt · 2017
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Transferring end-to-end visuomotor control from simulation to real world for a multi-stage task
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Hindsight experience replay
M. Andrychowicz, F. Wolski, A. Ray, J. Schneider, R. Fong, P. Welinder, B. McGrew, J. Tobin, P. Abbeel, and W. Zaremba · 2017
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M. Gualtieri and R. Platt · 2017
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Robotic pick-and-place of novel objects in clutter with multi-affordance grasping and cross-domain image matching
A. Zeng, S. Song, K.-T. Yu, E. Donlon, F. Hogan, M. Bauza, D. Ma, O. Taylor, M. Liu, E. Romo, et al · 2018
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Pick and place without geometric object models
M. Gualtieri, A. ten Pas, and R. Platt · 2018
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T. Lillicrap, J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra · 2015
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Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours
L. Pinto and A. Gupta · 2016
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Dex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics
J. Mahler, J. Liang, S. Niyaz, M. Laskey, R. Doan, X. Liu, J. A. Ojea, and K. Goldberg · 2017
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Learning hand-eye coordination for robotic grasping with large-scale data collection
S. Levine, P. Pastor, A. Krizhevsky, and D. Quillen
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End-to-end training of deep visuomotor policies
S. Levine, C. Finn, T. Darrell, and P. Abbeel
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Reinforcement Learning: An Introduction
R. Sutton and A. Barto · 2018
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Deep reinforcement learning for vision-based robotic grasping: A simulated comparative evaluation of off-policy methods
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