The DOI matches its Crossref record; 30 of the 31 references it cites were found in a registry or an index.
The YCB object and Model set: Towards common benchmarks for manipulation research
Abstract
In this paper we present the Yale-CMU-Berkeley (YCB) Object and Model set, intended to be used for benchmarking in robotic grasping and manipulation research. The objects in the set are designed to cover various aspects of the manipulation problem; it includes objects of daily life with different shapes, sizes, textures, weight and rigidity, as well as some widely used manipulation tests. The associated database provides high-resolution RGBD scans, physical properties and geometric models of the objects for easy incorporation into manipulation and planning software platforms. A comprehensive literature survey on existing benchmarks and object datasets is also presented and their scope and limitations are discussed. The set will be freely distributed to research groups worldwide at a series of tutorials at robotics conferences, and will be otherwise available at a reasonable purchase cost.
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In the citing paper
“is 72.4%, with per-object rates spanning 46.5%-88.7%, confirming generalization to novel object”
From Dexterous Robot Manipulation from Human Demonstrations via Contact-Anchored Retargeting and Residual Policy Learning (2026) · cites this paper as Calli et al., 2015
In the citing paper
“The detector was able to ignore the previously unseen distractors and pick up the target in 9 of 10 trials.”
From Domain randomization for transferring deep neural networks from simulation to the real world (2017) · cites this paper as [3]
In the citing paper
“The Distractor objects are sampled from a set of 78 object models taken from the YCB Object Dataset [6] and converted to CoppeliaSim compatible .ttm format.”
From THE COLOSSEUM: A Benchmark for Evaluating Generalization for Robotic Manipulation (2024) · cites this paper as [6]
Citations
Cites
ROS: an open-source Robot Operating System
Quigley, M. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · Cited by 8,729
Surface simplification using quadric error metrics
Garland, M., & Heckbert, P. S. (1997) · ACM SIGGRAPH Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH) · doi:10.1145/258734.258849 · Cited by 3,445
Adult Norms for the Box and Block Test of Manual Dexterity
Mathiowetz, V. G., Volland, G., Kashman, N., & Weber, K. (1985) · American Journal of Occupational Therapy · doi:10.5014/ajot.39.6.386 · Cited by 2,105
The Open Motion Planning Library
Sucan, I. A., Moll, M., & Kavraki, L. E. (2012) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2012.2205651 · Cited by 1,616
Shilane, P., Min, P., Kazhdan, M., & Funkhouser, T. A. (2004) · IEEE International Conference on Shape Modeling and Applications · doi:10.1109/smi.2004.1314504 · Cited by 1,461
CHOMP: Gradient optimization techniques for efficient motion planning
Ratliff, N., Zucker, M., Bagnell, J. A., & Srinivasa, S. S. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2009.5152817 · Cited by 1,052
Self-citationAdult Norms for the Nine Hole Peg Test of Finger Dexterity
Mathiowetz, V. G., Weber, K., Kashman, N., & Volland, G. (1985) · The Occupational Therapy Journal of Research · doi:10.1177/153944928500500102 · Cited by 973
Robotic Grasping of Novel Objects using Vision
Saxena, A., Driemeyer, J., & Ng, A. Y. (2008) · The International Journal of Robotics Research · doi:10.1177/0278364907087172 · Cited by 950
Chitta, S., Sucan, I. A., & Cousins, S. (2012) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2011.2181749 · Cited by 475
OpenRAVE: A Planning Architecture for Autonomous Robotics
Diankov, R., & Kuffner, J. (2008) · Cited by 381
Research works printed but not linked: 5
- richtsfeld, 2012, The Object Segmentation Database (OSD)
- 2005, McGill 3D Shape Benchmark
- 2014, Sketchup
- del pobil, 2006, Benchmarks for Robotics Research
- detry, 2014, International Workshop on Autonomous Grasping and Manipulation An Open Challenge
Cited by
Sorted by citations
Domain randomization for transferring deep neural networks from simulation to the real world
Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., & Abbeel, P. (2017) · 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8202133 · Cited by 3,124
“To test the performance of our object detectors on real-world objects with non-uniform textures, we trained an object detector to localize a can of Spam from the YCB Dataset [3].”
MethodSelf-citationPoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
Xiang, Y., Schmidt, T., Narayanan, V., & Fox, D. (2018) · Robotics: Science and Systems XIV · doi:10.15607/rss.2018.xiv.019 · Cited by 2,139
DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion
Wang, C., Xu, D., Zhu, Y., Martín-Martín, R., Lu, C., Fei-Fei, L., & Savarese, S. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00346 · Cited by 1,202
PVNet: Pixel-Wise Voting Network for 6DoF Pose Estimation
Peng, S., Liu, Y., Qi-xing Huang, A., Zhou, X., & Bao, H. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00469 · Cited by 1,059
PVN3D: A Deep Point-Wise 3D Keypoints Voting Network for 6DoF Pose Estimation
He, Y., Sun, W. M., Huang, H., Liu, J., Fan, H., & Sun, J. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01165 · Cited by 637
Metadata
- Authors
- Calli, B., Singh, A., Walsman, A., Srinivasa, S., Abbeel, P., & Dollar, A. M.
- Affiliations
- University of California, Berkeley (US)
- Carnegie Mellon University (US)
- Yale University (US)
- Venue
- 2015 International Conference on Advanced Robotics (ICAR)
- Publisher
- IEEE
- Published
- 2015-07
- Pages
- 510-517
- OpenAlex
- W1505952289
- Cited by
- 838
- References
- 31
- Citations by year
- 2026: 49 · 2025: 118 · 2024: 140 · 2023: 114 · 2022: 117 · 2021: 105 · 2020: 86 · 2019: 48 · 2018: 31 · 2017: 19 · 2016: 10 · 2015: 1
- FWCI
- 28.96
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Advanced Robotics (ICAR) (publishedVersion)
- Funders
- Yale University
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › 3D Shape Modeling and Analysis
- Keywords
- robotic manipulation, robotic grasping, benchmark dataset, object dataset, geometric model, manipulation planning, everyday objects
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