[robotics-worldwide] [news] Winners of the 2018 Amazon Robotics Best Paper Awards in Manipulation
Amazon Robotics is delighted to announce the winners of the first Amazon Robotics Best Paper Awards in Manipulation. Amazon.com is able to quickly package and ship millions of items to customers from a network of fulfillment centers all over the globe. This wouldn't be possible without leveraging cutting-edge advances in technology. Amazon's automated warehouses are successful at efficiently moving goods within a warehouse. However, commercially viable automated picking and stowing in unstructured environments still remains a difficult challenge.
To promote shared and open solutions to the technical challenges faced in unstructured automation and support innovative research in manipulation, Amazon Robotics sponsors a $5,000 award in each of the following two categories for an outstanding paper previously published in a major conference, journal, or open access archive:
* Best Technical Paper - awarded to an outstanding paper that describes a novel algorithm, method, or mechanism that inventively solves or simplifies a key problem in robotic manipulation.
* Best Systems Paper - awarded to an outstanding paper that describes the design of a robotic system for manipulation, including component selection, software architecture, or integration challenges that were overcome.
Today we are announcing those winners, as well as two Finalists in each category. We received so many high quality submissions that we decided to add the Finalist awards and offer a $1,000 award for those papers that stood out as the best we received this year. The 2018 award winners are:
Best Technical Paper Winner:
Peter R. Florence*, Lucas Manuelli*, and Russ Tedrake. Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulation. In Conference on Robot Learning (CoRL), Zurich, Switzerland (To Appear), October 2018. (* co-lead authorship)
Best Technical Paper Finalists:
S. Hasegawa, K. Wada, Y. Niitani, K. Okada and M. Inaba, "A three-fingered hand with a suction gripping system for picking various objects in cluttered narrow space," 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, 2017, pp. 1164-1171.
Donlon, E., Dong, S., Liu, M., Li, J., Adelson, E., & Rodriguez, A. (2018). GelSlim: A High-Resolution, Compact, Robust, and Calibrated Tactile-sensing Finger. IEEE/RSJ International Conference on Intelligent Robots (IROS)
Best Systems Paper Winner:
A. Zeng, S. Song, K.-T. Yu, E. Donlon, F. Hogan, M. Bauza, D. Ma, O. Taylor, M. Liu, E. Romo, N. Fazeli, F. Alet, N. Chavan-Dafle, R. Holladay, I. Morona, P. Q. Nair, D. Green, I. Taylor, W. Liu, T. Funkhouser, and A. Rodriguez, "Robotic pick-and-place of novel objects in clutter with multi-affordance grasping and cross-domain image matching," In IEEE International Conference on Robotics and Automation (ICRA), 2018.
Best Systems Paper Finalists:
Morrison, D., Tow, A.W., McTaggart, M., Smith, R., Kelly-Boxall, N., Wade-McCue, S., Erskine, J., Grinover, R., Gurman, A., Hunn, T. and Lee, D., 2017. Cartman: The low-cost cartesian manipulator that won the amazon robotics challenge. arXiv preprint arXiv:1709.06283.
Kappler, D., Meier, F., Issac, J., Mainprice, J., Cifuentes, C.G., Wüthrich, M., Berenz, V., Schaal, S., Ratliff, N. and Bohg, J., 2018. Real-time Perception meets Reactive Motion Generation. IEEE Robotics and Automation Letters, 3(3), pp.1864-1871.