The list of publications using/cited the YCB object and model set:
(If you would like your publication appear here, please email to email@example.com).
 Pedro Vicente, Lorenzo Jamone and Alexandre Bernardino, “Towards markerless visual servoing of grasping tasks for humanoid robots“, IEE International Conference on Robotics and Automation, 2017.
 Vezzani, G., Pattacini, U., and Natale, L., “A Grasping Approach Based on Superquadric Models“, in IEEE International Conference on Robotics and Automation, Singapore, 2017
 Culleton, Mark, Conor McGinn, and Kevin Kelly. “Framework for Assessing Robotic Dexterity within Flexible Manufacturing.” Journal of Intelligent & Robotic Systems, 1-231 2017
 Patel, Radhen, Rebecca Cox, Branden Romero, and Nikolaus Correll. “Improving grasp performance using in-hand proximity and contact sensing.” 2016 International Symposium on Experimental Robotics pp 185-194, 2017
 Stuart, Hannah, et al. “The Ocean One hands: An adaptive design for robust marine manipulation.” The International Journal of Robotics Research, pp. 150-166, 2017
 Beschi, M., E. Villagrossi, L. Molinari Tosatti, and D. Surdilovic. “Sensorless model-based object-detection applied on an underactuated adaptive hand enabling an impedance behavior.” Robotics and Computer-Integrated Manufacturing pp. 38-47, 2017
 Salvietti, Gionata, Leonardo Meli, Guido Gioioso, Monica Malvezzi, and Domenico Prattichizzo. “Multicontact Bilateral Telemanipulation With Kinematic Asymmetries.” IEEE/ASME Transactions on Mechatronics 22, pp. 445-456, 2017
 Deen Cockburn, Jean-Philippe Roberge, Thuy-Hong-Loan Le, Alexis Maslyczyk and Vincent Duchaine, “Grasp Stability Assessment through Unsupervised Feature Learning of Tactile Images”, IEEE International Conference on Robotics and Automation (ICRA), 2017
 Lorenzo Jamone, Alexandre Bernardino, and José Santos-Victor, “Benchmarking the Grasping Capabilities of the iCub Hand With the YCB Object and Model Set,” IEEE Robotics and Automation Letters, vol. 1, no. 1, Jan 2016.
 Burka, A., Hu, S., Helgeson, S., Krishnan, S., Gao, Y., Hendricks, L.A., Darrell, T. and Kuchenbecker, K.J., 2016, September. Proton: A visuo-haptic data acquisition system for robotic learning of surface properties. In 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 58-65, 2016
 I. Choi, E. W. Hawkes, D. L. Christensen, C. J. Ploch and S. Follmer, “Wolverine: A wearable haptic interface for grasping in virtual reality,” 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, pp. 986-993, 2016.
 M. V. Liarokapis; A. M. Dollar, “Post-Contact, In-Hand Object Motion Compensation With Adaptive Hands,” in IEEE Transactions on Automation Science and Engineering, pp.1-12, 2016.
 T. Suzuki and T. Oka, “Grasping of unknown objects on a planar surface using a single depth image,” 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), pp. 572-577. , 2016.
 Patel, Radhen, and Nikolaus Correll. “Integrated force and distance sensing using elastomer-embedded commodity proximity sensors.” In Proceedings of Robotics: Science and Systems. 2016.
 D. Chen, V. Dietrich and G. von Wichert, “Precision grasping based on probabilistic models of unknown objects,” 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 2044-2051, Stockholm, 2016.
 B. Lee and D. D. Lee, “Learning anisotropic ICP (LA-ICP) for robust and efficient 3D registration,” 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 5040-5045, Stockholm, 2016.
 Firman, Michael. “RGBD datasets: Past, present and future.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 19-31. 2016.
 Schiebener, David, et al. “Heuristic 3D object shape completion based on symmetry and scene context.” 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016.
 Rojas-de-Silva, Abiud, and Raúl Suárez. “Grasping bulky objects with two anthropomorphic hands.” In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 877-884. IEEE, 2016.
 Lee, Bhoram, and Daniel D. Lee. “Online learning of visibility and appearance for object pose estimation.” In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2792-2798, 2016.
 Benchmarking in Manipulation Research: The YCB Object and Model Set and Benchmarking Protocols, IEEE Robotics and Automation Magazine, pp. 36 – 52, Sept. 2015.
 The YCB Object and Model Set: Towards Common Benchmarks for Manipulation Research, proceedings of the 2015 IEEE International Conference on Advanced Robotics (ICAR), Istanbul, Turkey, 2015.