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Volumn 17, Issue , 2016, Pages

End-to-end training of deep visuomotor policies

Author keywords

Neural networks; Optimal control; Reinforcement learning; Vision

Indexed keywords

BOTTLES; NEURAL NETWORKS; VISION;

EID: 84979924150     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Review
Times cited : (3366)

References (93)
  • 6
    • 80051762104 scopus 로고    scopus 로고
    • Distributed optimization and statistical learning via the alternating direction method of multipliers
    • S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning, 3(1):1122, 2011.
    • (2011) Foundations and Trends in Machine Learning , vol.3 , Issue.1 , pp. 1122
    • Boyd, S.1    Parikh, N.2    Chu, E.3    Peleato, B.4    Eckstein, J.5
  • 10
    • 84862994574 scopus 로고    scopus 로고
    • Learning to control a low-cost manipulator using data-efficient reinforcement learning
    • M. Deisenroth, C. Rasmussen, and D. Fox. Learning to control a low-cost manipulator using data-efficient reinforcement learning. In Robotics: Science and Systems (RSS), 2011.
    • (2011) Robotics: Science and Systems (RSS)
    • Deisenroth, M.1    Rasmussen, C.2    Fox, D.3
  • 13
  • 18
    • 0019152630 scopus 로고
    • Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
    • K. Fukushima. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 36:193-202, 1980.
    • (1980) Biological Cybernetics , vol.36 , pp. 193-202
    • Fukushima, K.1
  • 22
    • 0025600638 scopus 로고
    • A stochastic reinforcement learning algorithm for learning real-valued functions
    • V. Gullapalli. A stochastic reinforcement learning algorithm for learning real-valued functions. Neural Networks, 3(6):671-692, 1990.
    • (1990) Neural Networks , vol.3 , Issue.6 , pp. 671-692
    • Gullapalli, V.1
  • 23
    • 0029387997 scopus 로고
    • Skillful control under uncertainty via direct reinforcement learning
    • V. Gullapalli. Skillful control under uncertainty via direct reinforcement learning. Reinforcement Learning and Robotics, 15(4):237-246, 1995.
    • (1995) Reinforcement Learning and Robotics , vol.15 , Issue.4 , pp. 237-246
    • Gullapalli, V.1
  • 28
    • 0026954775 scopus 로고
    • Neural networks for control systems: A survey
    • November
    • K. J. Hunt, D. Sbarbaro, R. Żbikowski, and P. J. Gawthrop. Neural networks for control systems: A survey. Automatica, 28(6):1083-1112, November 1992.
    • (1992) Automatica , vol.28 , Issue.6 , pp. 1083-1112
    • Hunt, K.J.1    Sbarbaro, D.2    Zbikowski, R.3    Gawthrop, P.J.4
  • 33
    • 84961612003 scopus 로고    scopus 로고
    • State representation learning in robotics: Using prior knowledge about physical interaction
    • R. Jonschkowski and O. Brock. State representation learning in robotics: Using prior knowledge about physical interaction. In Proceedings of Robotics: Science and Systems, 2014.
    • (2014) Proceedings of Robotics: Science and Systems
    • Jonschkowski, R.1    Brock, O.2
  • 47
    • 84930630277 scopus 로고    scopus 로고
    • Deep learning
    • May
    • Y. LeCun, Y. Bengio, and G. Hinton. Deep learning. Nature, 521:436-444, May 2015.
    • (2015) Nature , vol.521 , pp. 436-444
    • LeCun, Y.1    Bengio, Y.2    Hinton, G.3
  • 49
    • 84965098612 scopus 로고    scopus 로고
    • DeepMPC: Learning deep latent features for model predictive control
    • Ian Lenz, Ross Knepper, and Ashutosh Saxena. DeepMPC: Learning deep latent features for model predictive control. In RSS, 2015a.
    • (2015) RSS
    • Lenz, I.1    Knepper, R.2    Saxena, A.3
  • 50
    • 84928013181 scopus 로고    scopus 로고
    • Deep learning for detecting robotic grasps
    • Ian Lenz, Honglak Lee, and Ashutosh Saxena. Deep learning for detecting robotic grasps. IJRR, 2015b.
    • (2015) IJRR
    • Lenz, I.1    Lee, H.2    Saxena, A.3
  • 57
    • 17444424051 scopus 로고    scopus 로고
    • Iterative linear quadratic regulator design for nonlinear biological movement systems
    • W. Li and E. Todorov. Iterative linear quadratic regulator design for nonlinear biological movement systems. In ICINCO (1), pages 222-229, 2004.
    • (2004) ICINCO (1) , pp. 222-229
    • Li, W.1    Todorov, E.2
  • 64
    • 85131220210 scopus 로고    scopus 로고
    • Combining the benefits of function approximation and trajectory optimization
    • I. Mordatch and E. Todorov. Combining the benefits of function approximation and trajectory optimization. In Robotics: Science and Systems (RSS), 2014.
    • (2014) Robotics: Science and Systems (RSS)
    • Mordatch, I.1    Todorov, E.2
  • 69
    • 44949241322 scopus 로고    scopus 로고
    • Reinforcement learning of motor skills with policy gradients
    • J. Peters and S. Schaal. Reinforcement learning of motor skills with policy gradients. Neural Networks, 21(4):682-697, 2008.
    • (2008) Neural Networks , vol.21 , Issue.4 , pp. 682-697
    • Peters, J.1    Schaal, S.2
  • 71
    • 84979897279 scopus 로고    scopus 로고
    • Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours
    • abs/1509.06825
    • Lerrel Pinto and Abhinav Gupta. Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours. CoRR, abs/1509.06825, 2015.
    • (2015) CoRR
    • Pinto, L.1    Gupta, A.2
  • 73
    • 84862273266 scopus 로고    scopus 로고
    • A reduction of imitation learning and structured prediction to no-regret online learning
    • S. Ross, G. Gordon, and A. Bagnell. A reduction of imitation learning and structured prediction to no-regret online learning. Journal of Machine Learning Research, 15:627-635, 2011.
    • (2011) Journal of Machine Learning Research , vol.15 , pp. 627-635
    • Ross, S.1    Gordon, G.2    Bagnell, A.3
  • 77
    • 84910651844 scopus 로고    scopus 로고
    • Deep learning in neural networks: An overview
    • J. Schmidhuber. Deep learning in neural networks: An overview. Neural Networks, 61: 85-117, 2015.
    • (2015) Neural Networks , vol.61 , pp. 85-117
    • Schmidhuber, J.1
  • 80
    • 84979897556 scopus 로고    scopus 로고
    • Robobarista: Object part based transfer of manipulation trajectories from crowd-sourcing in 3d pointclouds
    • abs/1504.03071
    • Jaeyong Sung, Seok Hyun Jin, and Ashutosh Saxena. Robobarista: Object part based transfer of manipulation trajectories from crowd-sourcing in 3d pointclouds. CoRR, abs/1504.03071, 2015.
    • (2015) CoRR
    • Sung, J.1    Jin, S.H.2    Saxena, A.3
  • 90
    • 0000337576 scopus 로고
    • Simple statistical gradient-following algorithms for connectionist reinforcement learning
    • May
    • R. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8(3-4):229-256, May 1992.
    • (1992) Machine Learning , vol.8 , Issue.3-4 , pp. 229-256
    • Williams, R.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.