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GraspIt!
Abstract
A robotic grasping simulator, called Graspit!, is presented as versatile tool for the grasping community. The focus of the grasp analysis has been on force-closure grasps, which are useful for pick-and-place type tasks. This work discusses the different types of world elements and the general robot definition, and presented the robot library. The paper also describes the user interface of Graspit! and present the collision detection and contact determination system. The grasp analysis and visualization method were also presented that allow a user to evaluate a grasp and compute optimal grasping forces. A brief overview of the dynamic simulation system was provided.
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Cited by
Supersizing self-supervision: Learning to grasp from 50K tries and 700 robot hours
Pinto, L., & Gupta, A. (2016) · 2016 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2016.7487517 · Cited by 1,047
Real-time grasp detection using convolutional neural networks
Redmon, J., & Angelova, A. (2015) · 2015 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2015.7139361 · Cited by 893
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
Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations
Rajeswaran, A., Kumar, V., Gupta, A., Vezzani, G., Schulman, J., Todorov, E., & Levine, S. (2018) · Robotics: Science and Systems XIV · doi:10.15607/rss.2018.xiv.049 · Cited by 847
A Survey of Research on Cloud Robotics and Automation
Kehoe, B., Patil, S., Abbeel, P., & Goldberg, K. (2015) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2014.2376492 · Cited by 833
Metadata
- Authors
- Miller, A. T., & Allen, P. K.
- Venue
- IEEE Robotics & Automation Magazine
- Published
- 2004-12
- Volume
- 11
- Issue
- 4
- Pages
- 110-122
- ISSN
- 1070-9932 (ISSN-L)
- OpenAlex
- W1510186039
- Cited by
- 1,023
- References
- 0
- FWCI
- 24.98
- Type
- journal article
- Language
- en
- Open access
- closed
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- Miller, A. T., & Allen, P. K. (2004). GraspIt! IEEE Robotics & Automation Magazine. https://doi.org/10.1109/mra.2004.1371616
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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.
