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There has been significant recent work on data-driven algorithms for learning general-purpose grasping policies.
Bayesian grasping
K. Y. Goldberg and M. T. Mason · 1990
Earlier work this paper cites.
Orienting polygonal parts without sensors
K. Goldberg · 1993
Earlier work this paper cites.
Manipulating algebraic parts in the plane
A. S. Rao and K. Y. Goldberg · 1995
Earlier work this paper cites.
Part pose statistics: Estimators and experiments
K. Goldberg, B. V. Mirtich, Y. Zhuang, J. Craig, B. R. Carlisle, and J. Canny · 1999
Earlier work this paper cites.
Robotic grasping and contact: A review
A. Bicchi and V. Kumar · 2000
Earlier work this paper cites.
Manipulation of pose distributions
M. Moll and M. A. Erdmann · 2002
Earlier work this paper cites.
Finite-time analysis of the multiarmed bandit problem
P. Auer, N. Cesa-Bianchi, and P. Fischer · 2002
Earlier work this paper cites.
The expected hitting times for finite markov chains
H. Chen and F. Zhang · 2008
Earlier work this paper cites.
Combining active learning and reactive control for robot grasping
O. B. Kroemer, R. Detry, J. Piater, and J. Peters · 2010
Earlier work this paper cites.
Near-optimal regret bounds for reinforcement learning
T. Jaksh, R. Ortner, and P. Auer · 2010
Earlier work this paper cites.
Optimism in reinforcement learning and kullback-leibler divergence
S. Filippi, O. Cappé, and A. Garivier · 2010
Earlier work this paper cites.
Physically based grasp quality evaluation under pose uncertainty
J. Kim, K. Iwamoto, J. J. Kuffner, Y. Ota, and N. S. Pollard · 2013
Earlier work this paper cites.
Further optimal regret bounds for thompson sampling
S. Agrawal and N. Goyal · 2013
Earlier work this paper cites.
Near-optimal reinforcement learning in factored mdps
I. Osband and B. Van Roy · 2014
Earlier work this paper cites.
Leveraging big data for grasp planning
D. Kappler, J. Bohg, and S. Schaal · 2015
Cited alongside, same era.
Deep learning for detecting robotic grasps
I. Lenz, H. Lee, and A. Saxena · 2015
Cited alongside, same era.
Autonomously acquiring instance-based object models from experience
J. Oberlin and S. Tellex · 2015
Cited alongside, same era.
Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours
L. Pinto and A. Gupta · 2016
Cited alongside, same era.
A mathematical introduction to robotic manipulation
R. M. Murray · 2017
Cited alongside, same era.
Learning a visuomotor controller for real world robotic grasping using simulated depth images
U. Viereck, A. ten Pas, K. Saenko, and R. Platt · 2017
Exploration-exploitation in reinforcement learning
R. Fruit · 2018
Later among the works it cites.
Learning ambidextrous robot grasping policies
J. Mahler, M. Matl, V. Satish, M. Danielczuk, B. DeRose, S. McKinley, and K. Goldberg · 2019
Later among the works it cites.
The Mechanics of Robot Grasping
E. Rimon and J. Burdick · 2019
Later among the works it cites.
Comparing task simplifications to learn closed-loop object picking using deep reinforcement learning
M. Breyer, F. Furrer, T. Novkovic, R. Siegwart, and J. Nieto · 2019
Later among the works it cites.
A review of robot learning for manipulation: Challenges, representations, and algorithms
O. Kroemer, S. Niekum, and G. Konidaris · 2019
Later among the works it cites.
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Cited alongside, same era.
Visual detection of opportunities to exploit contact in grasping using contextual multi-armed bandits
C. Eppner and O. Brock · 2017
Cited alongside, same era.
Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation
D. Kalashnikov, A. Irpan, P. Pastor, J. Ibarz, A. Herzog, E. Jang, D. Quillen, E. Holly, M. Kalakrishnan, V. Vanhoucke, et al · 2018
Cited alongside, same era.
Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection
S. Levine, P. Pastor, A. Krizhevsky, J. Ibarz, and D. Quillen · 2018
Cited alongside, same era.
Learning object grasping for soft robot hands
C. Choi, W. Schwarting, J. DelPreto, and D. Rus · 2018
Cited alongside, same era.
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 · 2018
Cited alongside, same era.
Near-optimal reinforcement learning in factored mdps: Oracle-efficient algorithms for the non-episodic setting
Z. Xu and A. Tewari · 2018
Cited alongside, same era.
D. Wang, D. Tseng, P. Li, Y. Jiang, M. Guo, M. Danielczuk, J. Mahler, J. Ichnowski, and K. Goldberg · 2019
Later among the works it cites.
Robust toppling for vacuum suction grasping
C. Correa, J. Mahler, M. Danielczuk, and K. Goldberg · 2019
Later among the works it cites.
Near-optimal optimistic reinforcement learning using empirical bernstein inequalities
A. Tossou, D. Basu, and C. Dimitrakakis · 2019
Later among the works it cites.
Learning robust, real-time, reactive robotic grasping
D. Morrison, P. Corke, and J. Leitner · 2020
Closest in time.
Non-markov policies to reduce sequential failures in robot bin picking
K. Sanders, M. Danielczuk, J. Mahler, A. Tanwani, and K. Goldberg · 2020
Closest in time.
Accelerating grasp exploration by leveraging learned priors
K. Li, M. Danielczuk, A. Balakrishna, V. Satish, and K. Goldberg · 2020
Closest in time.
Multi-fingered active grasp learning
Q. Lu, M. Van der Merwe, and T. Hermans · 2020
Closest in time.
Egad! an evolved grasping analysis dataset for diversity and reproducibility in robotic manipulation
D. Morrison, P. Corke, and J. Leitner · 2020
Closest in time.