The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Efficient grasping from RGBD images: Learning using a new rectangle representation
Abstract
Given an image and an aligned depth map of an object, our goal is to estimate the full 7-dimensional gripper configuration—its 3D location, 3D orientation and the gripper opening width. Recently, learning algorithms have been successfully applied to grasp novel objects—ones not seen by the robot before. While these approaches use low-dimensional representations such as a ‘grasping point’ or a ‘pair of points’ that are perhaps easier to learn, they only partly represent the gripper configuration and hence are sub-optimal. We propose to learn a new ‘grasping rectangle’ representation: an oriented rectangle in the image plane. It takes into account the location, the orientation as well as the gripper opening width. However, inference with such a representation is computationally expensive. In this work, we present a two step process in which the first step prunes the search space efficiently using certain features that are fast to compute. For the remaining few cases, the second step uses advanced features to accurately select a good grasp. In our extensive experiments, we show that our robot successfully uses our algorithm to pick up a variety of novel objects.
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Cites
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Robotic grasping and contact: a review
Bicchi, A., & Kumar, V. (2002) · Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065) · doi:10.1109/robot.2000.844081 · Cited by 1,069
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(2005) · Cited by 981
Robotic Grasping of Novel Objects using Vision
Saxena, A., Driemeyer, J., & Ng, A. Y. (2008) · The International Journal of Robotics Research · doi:10.1177/0278364907087172 · Cited by 923
Self-citationRGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments
Henry, P., Krainin, M., Herbst, E., Ren, X., & Fox, D. (2014) · Springer Tracts in Advanced Robotics · doi:10.1007/978-3-642-28572-1_33 · Cited by 809
Automatic grasp planning using shape primitives
Miller, A. T., Knoop, S., Christensen, H. I., & Allen, P. K. (2004) · 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422) · doi:10.1109/robot.2003.1241860 · Cited by 705
Cited by
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 1,634
Self-citationSupersizing 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
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
Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration 0
O’Neill, A., Rehman, A., Maddukuri, A., Gupta, A., Padalkar, A., Lee, A., Pooley, A., Gupta, A., Mandlekar, A., Jain, A., et al. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra57147.2024.10611477 · Cited by 210
Metadata
- Authors
- Jiang, Y., Moseson, S., & Saxena, A.
- Venue
- 2011 IEEE International Conference on Robotics and Automation
- Publisher
- IEEE
- Published
- 2011-05
- Pages
- 3304-3311
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2123435073
- Cited by
- 657
- References
- 33
- FWCI
- 18.78
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Jiang, Y., Moseson, S., & Saxena, A. (2011). Efficient grasping from RGBD images: Learning using a new rectangle representation. 2011 IEEE International Conference on Robotics and Automation. https://doi.org/10.1109/icra.2011.5980145
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