The list of publications using/cited the YCB object and model set:
(If you would like your publication appear here, please email to bcalli@wpi.edu).
2019:
Immaculada Llop-Harillo, Antonio Pérez-González, Julia Starke, Tamim Asfour,
“The Anthropomorphic Hand Assessment Protocol (AHAP),”
Robotics and Autonomous Systems,
Volume 121,
2019,
2017:
[22] 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.
[21] Vezzani, G., Pattacini, U., and Natale, L., “A Grasping Approach Based on Superquadric Models“, in IEEE International Conference on Robotics and Automation, Singapore, 2017
[20] Culleton, Mark, Conor McGinn, and Kevin Kelly. “Framework for Assessing Robotic Dexterity within Flexible Manufacturing.” Journal of Intelligent & Robotic Systems, 1-231 2017
[19] 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
[18] 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
[17] 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
[16] 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
[15] 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
2016:
[14] 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.
[13] 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
[12] 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.
[11] 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.
[10] 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.
[9] Patel, Radhen, and Nikolaus Correll. “Integrated force and distance sensing using elastomer-embedded commodity proximity sensors.” In Proceedings of Robotics: Science and Systems. 2016.
[8] 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.
[7] 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.
[6] 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.
[5] 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.
[4] 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.
[3] 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.
2015:
[2] Benchmarking in Manipulation Research: The YCB Object and Model Set and Benchmarking Protocols, IEEE Robotics and Automation Magazine, pp. 36 – 52, Sept. 2015.
[1] 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.
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