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ContactGrasp: Functional Multi-finger Grasp Synthesis from Contact
Open accessAbstract
Grasping and manipulating objects is an important human skill. Since most objects are designed to be manipulated by human hands, anthropomorphic hands can enable richer human-robot interaction. Desirable grasps are not only stable, but also functional: they enable post-grasp actions with the object. However, functional grasp synthesis for high degree-of-freedom anthropomorphic hands from object shape alone is challenging. We present ContactGrasp, a framework for functional grasp synthesis from object shape and contact on the object surface. Contact can be manually specified or obtained through demonstrations. Our contact representation is object-centric and allows functional grasp synthesis even for hand models different than the one used for demonstration. Using a dataset of contact demonstrations from humans grasping diverse household objects, we synthesize functional grasps for three hand models and two functional intents. The project webpage is https://contactdb.cc.gatech.edu/contactgrasp.html.
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“the contact forces that distinguish stable grasps from infeasible poses (Brahmbhatt et al., 2019) are unrecorded”
From Dexterous Robot Manipulation from Human Demonstrations via Contact-Anchored Retargeting and Residual Policy Learning (2026) · cites this paper as Brahmbhatt et al., 2019
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Hao, Y., Zhang, J., Zhuo, T., Wen, F., & Fan, H. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i3.27979 · Cited by 5
CPF: Learning a Contact Potential Field to Model the Hand-Object InteractionPreprint
Yang, L., Zhan, X., Li, K., Xu, W., Li, J., & Lu, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.00924 · Cited by 5
Learning Cross-Hand Policies of High-DOF Reaching and Grasping
She, Q., Zhang, S., Ye, Y., Hu, R., & Xu, K. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73404-5_16 · Cited by 4
CMG-Net: An End-to-End Contact-based Multi-Finger Dexterous Grasping Network
Wei, M., Huang, Y., Xu, Z., Liu, N., Che, Z., Zhang, X., Shen, C., Feng, F., Shan, C., & Tang, J. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161481 · Cited by 4
AViTa: Adaptive Visual-Tactile Dexterous Grasping
Yan, H., Fang, H.-S., & Lu, C. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3461551 · Cited by 4
PEAR: phrase-based hand-object interaction anticipation
Zhang, Z., Luo, H., Zhai, W., Cao, Y. H., & Kang, Y. (2025) · Science China Information Sciences · doi:10.1007/s11432-024-4405-4 · Cited by 14
Hand Grasp Pose Prediction Based on Motion Prior Field
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TPGP: Temporal-Parametric Optimization with Deep Grasp Prior for Dexterous Motion Planning
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FastGrasp: Efficient Grasp Synthesis with Diffusion
Wu, X., Liu, T., Li, C., Ma, Y., Shi, Y., & He, X. (2025) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv66043.2025.00073 · Cited by 5
One-shot learning for autonomous aerial manipulation
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Synthetic contact maps to predict grasp regions on objects
Martínez-González, P., Mulero-Pérez, D., Oprea, S., Benavent-Lledó, M., Orts‐Escolano, S., & García‐Rodríguez, J. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892548 · Cited by 2
Joint Hand and Object Pose Estimation from a Single RGB Image using High‐level 2D Constraints
Song, H., Mu, T., & Martin, R. R. (2022) · Computer Graphics Forum · doi:10.1111/cgf.14685 · Cited by 2
Parallel-Jaw Gripper and Grasp Co-Optimization for Sets of Planar Objects
Jiang, R., Doshi, N., Gondhalekar, R., & Rodríguez, A. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10342241 · Cited by 2
A Surprisingly Efficient Representation for Multi-Finger Grasping
Yan, H., Fang, H.-S., & Lu, C. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611424 · Cited by 2
Liu, T., Liu, Z., Jiao, Z., Zhu, Y., & Zhu, S. C. (2021) · arXiv (Cornell University) · Cited by 2
Target Pose Guided Whole-body Grasping Motion Generation for Digital Humans
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Metadata
- Authors
- Brahmbhatt, S., Handa, A., Hays, J., & Fox, D.
- Affiliations
- Georgia Institute of Technology (US)
- Nvidia (United States) (US)
- Venue
- 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Publisher
- IEEE
- Published
- 2019-11
- Pages
- 2386-2393
- ISSN
- 2153-0858 (ISSN-L)
- OpenAlex
- W2934245910
- Cited by
- 125
- References
- 40
- Citations by year
- 2026: 6 · 2025: 19 · 2024: 34 · 2023: 26 · 2022: 22 · 2021: 11 · 2020: 6 · 2019: 1
- FWCI
- 15.46
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Engineering › Biomedical Engineering › Soft Robotics and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- anthropomorphic hand, object shape, human-robot interaction, household products, grasp stability
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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