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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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“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]
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Lundell, J., Verdoja, F., Le, T. N., Mousavian, A., Fox, D., & Kyrki, V. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341344 · Cited by 5
Spatial and temporal consistency learning for monocular 6D pose estimation
Zhang, H., Liang, J., Hong, J.-X., Lei, Q., Liu, J., & Du, J. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107803 · Cited by 5
Learning to Live Life on the Edge: Online Learning for Data-Efficient Tactile Contour Following
Stone, E. A., Lepora, N. F., & Barton, D. A. W. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341565 · Cited by 5
What Can Robotics Research Learn from Computer Vision Research?
Corke, P., Dayoub, F., Hall, D., Skinner, J., & Sünderhauf, N. (2022) · Springer proceedings in advanced robotics · doi:10.1007/978-3-030-95459-8_61 · Cited by 5
Robot Finger with Remote Center of Motion Mechanism for Covering Joints with Thick Skin
Hsu, C.-C., Schmitz, A., Kusayanagi, K., & Sugano, S. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968235 · Cited by 5
Simultaneous Tactile Exploration and Grasp Refinement for Unknown Objects
de Farias, C., Marturi, N., Stolkin, R., & Bekiroglu, Y. (2021) · Chalmers Research (Chalmers University of Technology) · Cited by 5
Evaluating Real-World Robot Manipulation Policies in Simulation
Li, X., Hsu, K., Gu, J., Pertsch, K., Mees, O., Walke, H. R., Fu, C., Lunawat, I., Sieh, I., Kirmani, S., et al. (2024) · arXiv · doi:10.48550/arxiv.2405.05941 · Cited by 4
“Simulated evaluations have great potential as a tool for practitioners: they can reduce the need for costly real-world evaluations and provide a more comprehensive picture of a policy’s performance by enabling sweeps across a wide range of controlled environment conditions at negligible added cost.”
Adapting Pre-Trained Vision Models for Novel Instance Detection and Segmentation
Lu, Y., Jishnu Jaykumar, P., Guo, Y., Ruozzi, N., & Yu, X. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11245990 · Cited by 16
DexH2R: Task-Oriented Dexterous Manipulation From Human to Robots
Zhao, S., Zhu, X., Chen, Y., Li, C., Xie, Y., Zhang, X., Ding, M., & Tomizuka, M. (2025) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2025.3641164 · Cited by 4
Tactile-reactive gripper with an active palm for dexterous manipulation
Zhou, Y., Lee, W. S., Gu, Y., & She, Y. (2026) · npj Robotics · doi:10.1038/s44182-026-00079-y · Cited by 4
Collision Detection between Smooth Convex Bodies via Riemannian Optimization Framework
An, S., Lee, S., Lee, J., Park, S., & Lee, D. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802286 · Cited by 4
GraspMixer: Hybrid of Contact Surface Sampling and Grasp Feature Mixing for Grasp Synthesis
Lee, H., Toner, T., Tilbury, D. M., & Barton, K. L. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3530795 · Cited by 4
Toward Biosignals-Free Autonomous Prosthetic Hand Control via Imitation Learning
Shi, K., Lu, W., Zhao, H., da Fonseca, V. P., Zou, T., & Jiang, X. (2025) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2025.3605579 · Cited by 5
Hanai, R., Harada, K., Hara, I., & Ando, N. (2018) · ROBOMECH Journal · doi:10.1186/s40648-018-0120-z · Cited by 4
Co-optimization of robotic design and skill inspired by human hand evolution
Yang, B., Jiang, L., Bao, G., Yu, H., & Zhou, X. (2022) · Bioinspiration & Biomimetics · doi:10.1088/1748-3190/ac884e · Cited by 4
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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