Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection

Keywords

Artificial intelligence, Visual servoing, GRASP, Computer vision, Eye–hand coordination

References

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• Leveraging big data for grasp planningDaniel Kappler, Jeannette Bohg, Stefan Schaal — 2015
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• Real-time grasp detection using convolutional neural networksJoseph Redmon, Anelia Angelova — 2014
• Learning descriptors for object recognition and 3D pose estimationPaul Wohlhart, Vincent Lepetit — 2015
• Autonomous online generation of a motor representation of the workspace for intelligent whole-body reachingLorenzo Jamone, Martim Brandão, Lorenzo Natale — 2014
• Learning grasp affordance densitiesRenaud Detry, Dirk Kraft, Oliver Kroemer — 2011
• From caging to graspingAlberto Rodríguez, Matthew T. Mason, Steve Ferry — 2011
• Cloud-based robot grasping with the google object recognition engineBen Kehoe, Akihiro Matsukawa, Sal Candido — 2013
• Deep learning for detecting robotic graspsIan Lenz, Honglak Lee, Ashutosh Saxena — 2013
• Acquiring visual servoing reaching and grasping skills using neural reinforcement learningThomas Lampe, Martin Riedmiller — 2013
• The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation, and Machine LearningLih‐Yuan Deng — 2006
• A Platform for Robotics Research Based on the Remote-Brained Robot ApproachMasayuki Inaba, Satoshi Kagami, Fumio Kanehiro — 2000
• End-to-end dexterous manipulation with deliberate interactive estimationNicolas Hudson, Thomas M. Howard, Jeremy Ma — 2012
• Data-Driven Grasp Synthesis—A SurveyJeannette Bohg, Antonio Morales, Tamim Asfour — 2013
• Uncalibrated Visual ServoingMichael Spratling, R. Cipolla — 1996
• Herb 2.0: Lessons Learned From Developing a Mobile Manipulator for the HomeSiddhartha S Srinivasa, Dmitry Berenson, Maya Çakmak — 2012
• A Survey of Research on Cloud Robotics and AutomationBen Kehoe, Sachin Patil, Pieter Abbeel — 2015

Cited by

• Hierarchical Haptic Manipulation for Complex Skill LearningSimon Hangl, Emre Uğur, Sándor Szedmák — 2016
• Interactive Perception: Leveraging Action in Perception and Perception in ActionJeannette Bohg, Karol Hausman, Bharath Sankaran — 2016
• The Curious Robot: Learning Visual Representations via Physical InteractionsLerrel Pinto, Dhiraj Gandhi, Yuanfeng Han — 2016
• Learning a Driving SimulatorEder Santana, George Hotz — 2016
• Deep learning a grasp function for grasping under gripper pose uncertaintyEdward Johns, Stefan Leutenegger, Andrew J. Davison — 2016
• Unsupervised Learning from Continuous Video in a Scalable Predictive Recurrent NetworkFilip Piȩkniewski, Patryk A. Laurent, Csaba Petre — 2016
• Classifying and sorting cluttered piles of unknown objects with robots: A learning approachJanne V. Kujala, Tuomas J. Lukka, Harri Holopainen — 2016
• Modeling Grasp Motor ImageryMatthew Veres — 2016
• Latest Datasets and Technologies Presented in the Workshop on Grasping and Manipulation DatasetsMatteo Bianchi, Jeannette Bohg, Yu Sun — 2016
• Sequential View Grasp Detection For Inexpensive Robotic ArmsMarcus Gualtieri, Robert W. Platt — 2016
• 3D Simulation for Robot Arm Control with Deep Q-LearningStephen James, Edward Johns — 2016
• Learning to push by grasping: Using multiple tasks for effective learningLerrel Pinto, Abhinav Gupta — 2016
• Deep visual foresight for planning robot motionChelsea Finn, Sergey Levine — 2016
• Supervision via competition: Robot adversaries for learning tasksLerrel Pinto, James Davidson, Abhinav Gupta — 2016
• Collective robot reinforcement learning with distributed asynchronous guided policy searchAli Abdullah Yahya, Adrian Li, Mrinal Kalakrishnan — 2016
• Vision-Based Reaching Using Modular Deep Networks: from Simulation to the Real WorldFangyi Zhang, Jürgen Leitner, Ben Upcroft — 2016
• Towards Lifelong Self-Supervision: A Deep Learning Direction for RoboticsJay Ming Wong — 2016
• Privacy-preserving Grasp Planning in the CloudJeffrey Mahler, Brian Hou, Sherdil Niyaz — 2016
• CoSTAR: Instructing collaborative robots with behavior trees and visionChris Paxton, Andrew Hundt, Felix Jonathan — 2016
• Learning to Perform Physics Experiments via Deep Reinforcement LearningMisha Denil, Pulkit Agrawal, Tejas D. Kulkarni — 2016

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