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We describe a learning-based approach to hand-eye coordination for robotic grasping from monocular images.
A New Approach to Visual Servoing in Robotics
Espiau, B., Chaumette, F., and Rives, P · 1992
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Active, uncalibrated visual servoing
Yoshimi, B. H. and Allen, P. K · 1994
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Convolutional networks for images, speech, and time series
LeCun, Y. and Bengio, Y · 1995
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Relative End-Effector Control Using Cartesian Position Based Visual Servoing
Wilson, W. J., Hulls, C. W. Williams, and Bell, G. S · 1996
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Experimental Evaluation of Uncalibrated Visual Servoing for Precision Manipulation
Jägersand, M., Fuentes, O., and Nelson, R. C · 1997
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A platform for robotics research based on the remote-brained robot approach
Inaba, M., Kagami, S., Kanehiro, F., and Hoshino, Y · 2000
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Survey on Visual Servoing for Manipulation
Kragic, D. and Christensen, H. I · 2002
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The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation, and Machine Learning
Rubinstein, R. and Kroese, D · 2004
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Springer Handbook of Robotics
Siciliano, B. and Khatib, O · 2007
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Fitted Q-Iteration in Continuous Action-Space MDPs
Antos, A., Szepesvari, C., and Munos, R · 2008
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Visual Servoing for Humanoid Grasping and Manipulation Tasks
Vahrenkamp, N., Wieland, S., Azad, P., Gonzalez, D., Asfour, T., and Dillmann, R · 2008
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Cloud-enabled humanoid robots
Kuffner, J · 2010
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Combined Shape, Appearance and Silhouette for Simultaneous Manipulator and Object Tracking
Hebert, P., Hudson, N., Ma, J., Howard, T., Fuchs, T., Bajracharya, M., and Burdick, J · 2012
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End-to-End Dexterous Manipulation with Deliberate Interactive Estimation
Hudson, N., Howard, T., Ma, J., Jain, A., Bajracharya, M., Myint, S., Kuo, C., Matthies, L., Backes, P., and Hebert, P · 2012
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ImageNet Classification with Deep Convolutional Neural Networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
Cited alongside, same era.
From Caging to Grasping
Rodriguez, A., Mason, M. T., and Ferry, S · 2012
Cited alongside, same era.
HERB 2.0: Lessons Learned from Developing a Mobile Manipulator for the Home
Srinivasa, S., Berenson, D., Cakmak, M., Romea, A. C., Dogar, M., Dragan, A., Knepper, R. A., Niemueller, T. D., Strabala, K., Vandeweghe, J. M., and Ziegler, J · 2012
Cited alongside, same era.
Pose Error Robust Grasping from Contact Wrench Space Metrics
Weisz, J. and Allen, P. K · 2012
Cited alongside, same era.
Photometric visual servoing for omnidirectional cameras
Caron, G., Marchand, E., and Mouaddib, E · 2013
Cited alongside, same era.
Cloud-based robot grasping with the google object recognition engine
Kehoe, B., Matsukawa, A., Candido, S., Kuffner, J., and Goldberg, K · 2013
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S. and Szegedy, C · 2015
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Leveraging Big Data for Grasp Planning
Kappler, D., Bohg, B., and Schaal, S · 2015
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A survey of research on cloud robotics and automation
Kehoe, B., Patil, S., Abbeel, P., and Goldberg, K · 2015
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Deep Learning for Detecting Robotic Grasps
Lenz, I., Lee, H., and Saxena, A · 2015
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End-to-end Training of Deep Visuomotor Policies
Levine, S., Finn, C., Darrell, T., and Abbeel, P · 2015
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Human-level control through deep reinforcement learning
Mnih, V. et al · 2015
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Cited alongside, same era.
Acquiring Visual Servoing Reaching and Grasping Skills using Neural Reinforcement Learning
Lampe, T. and Riedmiller, M · 2013
Cited alongside, same era.
Data-Driven Grasp Synthesis — A Survey
Bohg, J., Morales, A., Asfour, T., and Kragic, D · 2014
Cited alongside, same era.
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L · 2014
Cited alongside, same era.
Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., and Malik, J · 2014
Cited alongside, same era.
Learning of Grasp Selection based on Shape-Templates
Herzog, A., Pastor, P., Kalakrishnan, M., Righetti, L., Bohg, J., Asfour, T., and Schaal, S · 2014
Cited alongside, same era.
Using Near-Field Stereo Vision for Robotic Grasping in Cluttered Environments
Leeper, A., Hsiao, K., Chu, E., and Salisbury, J.K · 2014
Cited alongside, same era.
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Autonomously acquiring instance-based object models from experience
Oberlin, J. and Tellex, S · 2015
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Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours
Pinto, L. and Gupta, A · 2015
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Real-time grasp detection using convolutional neural networks
Redmon, J. and Angelova, A · 2015
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Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images
Watter, M., Springenberg, J., Boedecker, J., and Riedmiller, M · 2015
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Learning Descriptors for Object Recognition and 3D Pose Estimation
Wohlhart, P. and Lepetit, V · 2015
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Continuous control with deep reinforcement learning
Lillicrap, T., Hunt, J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., and Wierstra, D · 2016
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Dex-Net 1.0: A cloud-based network of 3D objects for robust grasp planning using a multi-armed bandit model with correlated rewards
Mahler, J., Pokorny, F., Hou, B., Roderick, M., Laskey, M., Aubry, M., Kohlhoff, K., Kröger, T., Kuffner, J., and Goldberg, K · 2016
Closest in time.