The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Dex-Net 1.0: A cloud-based network of 3D objects for robust grasp planning using a Multi-Armed Bandit model with correlated rewards
Abstract
This paper presents the Dexterity Network (Dex-Net) 1.0, a dataset of 3D object models and a sampling-based planning algorithm to explore how Cloud Robotics can be used for robust grasp planning. The algorithm uses a Multi- Armed Bandit model with correlated rewards to leverage prior grasps and 3D object models in a growing dataset that currently includes over 10,000 unique 3D object models and 2.5 million parallel-jaw grasps. Each grasp includes an estimate of the probability of force closure under uncertainty in object and gripper pose and friction. Dex-Net 1.0 uses Multi-View Convolutional Neural Networks (MV-CNNs), a new deep learning method for 3D object classification, to provide a similarity metric between objects, and the Google Cloud Platform to simultaneously run up to 1,500 virtual cores, reducing experiment runtime by up to three orders of magnitude. Experiments suggest that correlated bandit techniques can use a cloud-based network of object models to significantly reduce the number of samples required for robust grasp planning. We report on system sensitivity to variations in similarity metrics and in uncertainty in pose and friction. Code and updated information is available at http://berkeleyautomation.github.io/dex-net/.
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Cites
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VoxNet: A 3D Convolutional Neural Network for real-time object recognition
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Self-citation
Cited by
Levine, S., Pastor, P., Krizhevsky, A., Ibarz, J., & Quillen, D. (2018) · The International Journal of Robotics Research · doi:10.1177/0278364917710318 · Cited by 1,971
More Than a Feeling: Learning to Grasp and Regrasp Using Vision and Touch
Calandra, R., Owens, A., Jayaraman, D., Lin, J., Yuan, W., Malik, J., Adelson, E. H., & Levine, S. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2852779 · Cited by 359
Grasping in the Wild: Learning 6DoF Closed-Loop Grasping From Low-Cost Demonstrations
Song, S., Zeng, A., Lee, J., & Funkhouser, T. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.3004787 · Cited by 185
Deep learning in robotics: survey on model structures and training strategies
Karoly, A. I., Galambos, P., Kuti, J., & Rudas, I. J. (2021) · IEEE Transactions on Systems, Man, and Cybernetics: Systems · doi:10.1109/tsmc.2020.3018325 · Cited by 146
Where2Act: From Pixels to Actions for Articulated 3D Objects
Mo, K., Guibas, L., Mukadam, M., Gupta, A., & Tulsiani, S. (2021) · arXiv · doi:10.48550/arxiv.2101.02692 · Cited by 132
Metadata
- Authors
- Mahler, J., Pokorny, F. T., Hou, B., Roderick, M., Laskey, M., Aubry, M., Kohlhoff, K., Kroger, T., Kuffner, J., & Goldberg, K.
- Venue
- 2016 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2016-05
- Pages
- 1957-1964
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2414685554
- Cited by
- 359
- References
- 38
- FWCI
- 60.71
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Dex-Net 1.0: A cloud-based network of 3D objects for robust grasp planning using a Multi-Armed Bandit model with correlated rewards. https://app.cochat.ai/papers/10.1109/icra.2016.7487342
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- Mahler, J., Pokorny, F. T., Hou, B., Roderick, M., Laskey, M., Aubry, M., Kohlhoff, K., Kroger, T., Kuffner, J., & Goldberg, K. (2016). Dex-Net 1.0: A cloud-based network of 3D objects for robust grasp planning using a Multi-Armed Bandit model with correlated rewards. 2016 IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/icra.2016.7487342
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