The DOI matches its Crossref record; 39 of the 40 references it cites were found in a registry or an index.
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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Citations
Cites
Deep learning for detecting robotic grasps
Lenz, I., Lee, H., & Saxena, A. (2015) · The International Journal of Robotics Research · doi:10.1177/0278364914549607 · Cited by 1,683
Mahler, J., Liang, J., Niyaz, S., Laskey, M., Doan, R., Liu, X., Aparicio, J., & Goldberg, K. Y. (2017) · Robotics: Science and Systems · doi:10.15607/rss.2017.xiii.058 · Cited by 1,434
Robotic grasping and contact: a review
Bicchi, A., & Kumar, V. (2002) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2000.844081 · Cited by 1,135
Supersizing self-supervision: Learning to grasp from 50K tries and 700 robot hours
Pinto, L., & Gupta, A. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487517 · Cited by 1,099
Miller, A. T., & Allen, P. K. (2004) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2004.1371616 · Cited by 1,048
Real-time grasp detection using convolutional neural networks
Redmon, J., & Angelova, A. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7139361 · Cited by 929
Data-Driven Grasp Synthesis—A Survey
Bohg, J., Morales, A., Asfour, T., & Kragic, D. (2014) · IEEE Transactions on Robotics · doi:10.1109/tro.2013.2289018 · Cited by 929
Constructing Force- Closure Grasps
Nguyễn, V. Đ. (1988) · The International Journal of Robotics Research · doi:10.1177/027836498800700301 · Cited by 914
Ferrari, C., & Canny, J. (2003) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1992.219918 · Cited by 895
Efficient grasping from RGBD images: Learning using a new rectangle representation
Yun, J., Moseson, S., & Saxena, A. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5980145 · Cited by 684
Research works printed but not linked: 2
- 0, Barrett Hand
- 0, Allegro Hand
Cited by
Sorted by citations
GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping
Fang, H.-S., Wang, C., Gou, M., & Lu, C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01146 · Cited by 683
ContactPose: A Dataset of Grasps with Object Contact and Hand Pose
Brahmbhatt, S., Tang, C., Twigg, C. D., Kemp, C. C., & Hays, J. H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58601-0_22 · Cited by 222
Self-citationGrasping Field: Learning Implicit Representations for Human Grasps
Karunratanakul, K., Yang, J., Zhang, Y., Black, M. J., Muandet, K., & Tang, S. (2020) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv50981.2020.00043 · Cited by 207
BEHAVE: Dataset and Method for Tracking Human Object Interactions
Bhatnagar, B. L., Xie, X., Petrov, I. A., Sminchisescu, C., Theobalt, C., & Pons‐Moll, G. (2022) · 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/cvpr52688.2022.01547 · Cited by 220
Hand-Object Contact Consistency Reasoning for Human Grasps Generation
Jiang, H., Liu, S., Wang, J., & Wang, X. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01092 · Cited by 170
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
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