The DOI matches its Crossref record.
Examples of 3D grasp quality computations
Open accessAbstract
Previous grasp quality research is mainly theoretical, and has assumed that contact types and positions are given, in order to preserve the generality of the proposed quality measures. The example results provided by these works either ignore hand geometry and kinematics entirely or involve only the simplest of grippers. We present a unique grasp analysis system that, when given a 3D object, hand, and pose for the hand, can accurately determine the types of contacts that will occur between the links of the hand and the object, and compute two measures of quality for the grasp. Using models of two articulated robotic hands, we analyze several grasps of a polyhedral model of a telephone handset, and we use a novel technique to visualize the 6D space used in these computations. In addition, we demonstrate the possibility of using this system for synthesizing high quality grasps by performing a search over a subset of possible hand configurations.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
ContactGrasp: Functional Multi-finger Grasp Synthesis from Contact
Brahmbhatt, S., Handa, A., Hays, J., & Fox, D. (2019) · 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8967960 · Cited by 125
Metadata
- Authors
- Miller, A. T., & Allen, P. K.
- Venue
- Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C)
- Publisher
- IEEE
- Published
- 2003-01-20
- Volume
- 2
- Pages
- 1240-1246
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2167788274
- Cited by
- 193
- References
- 0
- FWCI
- 7.41
- Type
- conference paper
- Language
- en
- Open access
- green
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Examples of 3D grasp quality computations. https://app.cochat.ai/papers/10.1109/robot.1999.772531
- The paper
- Miller, A. T., & Allen, P. K. (2003). Examples of 3D grasp quality computations. Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C). https://doi.org/10.1109/robot.1999.772531
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
