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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The shared policy, without fine-tuning, achieved a pooled success rate of 72.4% on six unseen YCB objects, with per-object rates ranging from 46.5% to 88.7%.
“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
The detector trained to localize a can of Spam from the YCB Dataset was able to ignore previously unseen food-item distractors and pick up the target in 9 of 10 trials.
“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]
Distractor objects are sampled from a set of 78 object models taken from the YCB Object Dataset.
“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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Learning-Based Control Strategies for Soft Robots: Theory, Achievements, and Future Challenges
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Benchmarks for Aerial Manipulation
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Multi-FinGAN: Generative Coarse-To-Fine Sampling of Multi-Finger Grasps
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Multi-Modal Geometric Learning for Grasping and Manipulation
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Uni6D: A Unified CNN Framework without Projection Breakdown for 6D Pose Estimation
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- 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 › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Keywords
- robotic manipulation, robotic grasping, benchmark dataset, object dataset, geometric model, manipulation planning, everyday objects
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