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Learning-based approaches for robotic grasping using visual sensors typically require collecting a large size dataset, either manually labeled or by many trial and errors of a robotic manipulator in the real or simulated world.
Nguyen, V.D.: Constructing force-closure grasps. In: Proceedings. 1986 IEEE International Conference on Robotics and Automation. vol. 3, pp. 1368–1373 (Apr 1986)
1986
Earlier work this paper cites.
Schaal, S.: Learning from demonstration. In: Proceedings of the 9th International Conference on Neural Information Processing Systems. pp. 1040–1046 (1996)
1996
Earlier work this paper cites.
Haddadin, S., Albu-Schaffer, A., Luca, A.D., Hirzinger, G.: Collision detection and reaction: A contribution to safe physical human-robot interaction. In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. pp. 3356–3363 (Sept 2008)
2008
Earlier work this paper cites.
Peters, J., Schaal, S.: Reinforcement learning of motor skills with policy gradients. Neural Networks 21
2008
Earlier work this paper cites.
Saxena, A., Driemeyer, J., Ng, A.Y.: Robotic grasping of novel objects using vision. The International Journal of Robotics Research 27
2008
Earlier work this paper cites.
Goldfeder, C., Ciocarlie, M., Dang, H., Allen, P.K.: The columbia grasp database. In: Proceedings of the 2009 IEEE International Conference on Robotics and Automation. pp. 3343–3349 (2009)
2009
Earlier work this paper cites.
Pastor, P., Hoffmann, H., Asfour, T., Schaal, S.: Learning and generalization of motor skills by learning from demonstration. In: 2009 IEEE International Conference on Robotics and Automation. pp. 763–768 (May 2009)
2009
Earlier work this paper cites.
Tegin, J., Ekvall, S., Kragic, D., Wikander, J., Iliev, B.: Demonstration-based learning and control for automatic grasping. Intelligent Service Robotics 2
2009
Earlier work this paper cites.
Bohg, J., Morales, A., Asfour, T., Kragic, D.: Data-driven grasp synthesis - a survey. IEEE Transactions on Robotics 30
2014
Cited alongside, same era.
Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: Delving deep into convolutional nets. In: BMVC (2014)
2014
Cited alongside, same era.
2014
Cited alongside, same era.
Redmon, J., Angelova, A.: Real-time grasp detection using convolutional neural networks (December 2014)
2014
Cited alongside, same era.
Bloss, R.: Collaborative robots are rapidly providing major improvements in productivity, safety, programing ease, portability and cost while addressing many new applications. Industrial Robot: the international journal of robotics research and application 43
Gu, S., Holly, E., Lillicrap, T., Levine, S.: Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates. In: 2017 IEEE International Conference on Robotics and Automation (ICRA). pp. 3389–3396 (May 2017)
2017
Later among the works it cites.
Levine, S., Pastor, P., Krizhevsky, A., Ibarz, J., Quillen, D.: Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection. The International Journal of Robotics Research (2017)
2017
Later among the works it cites.
2017
Later among the works it cites.
Franka EMIKA. http://www.franka.de/ , last accessed April 6, 2018
2018
Closest in time.
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2016
Cited alongside, same era.
Levine, S., Finn, C., Darrell, T., Abbeel, P.: End-to-end training of deep visuomotor policies. Journal of Machine Learning Research 17
2016
Cited alongside, same era.
Pinto, L., Gupta, A.: Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours. In: 2016 IEEE International Conference on Robotics and Automation (ICRA). pp. 3406–3413 (May 2016)
2016
Cited alongside, same era.
Kuka AG. http://www.kuka.com/ , last accessed April 6, 2018
2018
Closest in time.
Universal robots. http://www.universal-robots.com/ , last accessed April 6, 2018
2018
Closest in time.
Nichol, A., Schulman, J.: Reptile: a scalable metalearning algorithm (March 2018)
2018
Closest in time.