The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching
Abstract
This paper presents a robotic pick-and-place system that is capable of grasping and recognizing both known and novel objects in cluttered environments. The key new feature of the system is that it handles a wide range of object categories without needing any task-specific training data for novel objects. To achieve this, it first uses a category-agnostic affordance prediction algorithm to select and execute among four different grasping primitive behaviors. It then recognizes picked objects with a cross-domain image classification framework that matches observed images to product images. Since product images are readily available for a wide range of objects (e.g., from the web), the system works out-of-the-box for novel objects without requiring any additional training data. Exhaustive experimental results demonstrate that our multi-affordance grasping achieves high success rates for a wide variety of objects in clutter, and our recognition algorithm achieves high accuracy for both known and novel grasped objects. The approach was part of the MIT-Princeton Team system that took 1st place in the stowing task at the 2017 Amazon Robotics Challenge. All code, datasets, and pre-trained models are available online at http://arc.cs.princeton.edu.
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Cites
Deep Residual Learning for Image Recognition
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Fully convolutional networks for semantic segmentation
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Automatic grasp planning using shape primitives
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Cited by
Learning Synergies Between Pushing and Grasping with Self-Supervised Deep Reinforcement Learning
Zeng, A., Song, S., Welker, S., Lee, J., Rodriguez, A., & Funkhouser, T. (2018) · 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593986 · Cited by 606
Self-citationTossingBot: Learning to Throw Arbitrary Objects With Residual Physics
Zeng, A., Song, S., Lee, J., Rodriguez, A., & Funkhouser, T. (2020) · IEEE Transactions on Robotics · doi:10.1109/tro.2020.2988642 · Cited by 282
Self-citationCLIPort: What and Where Pathways for Robotic Manipulation
Shridhar, M., Manuelli, L., & Fox, D. (2021) · arXiv · doi:10.48550/arxiv.2109.12098 · Cited by 223
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
Self-citationSeita, D., Florence, P., Tompson, J., Coumans, E., Sindhwani, V., Goldberg, K., & Zeng, A. (2021) · 2021 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48506.2021.9561391 · Cited by 130
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Metadata
- Authors
- Zeng, A., Song, S., Yu, K.-T., Donlon, E., Hogan, F. R., Bauza, M., Ma, D., Taylor, O., Liu, M., Romo, E., Fazeli, N., Alet, F., Dafle, N. C., Holladay, R., Morena, I., Qu Nair, P., Green, D., Taylor, I., Liu, W., Funkhouser, T., & Rodriguez, A.
- Venue
- 2018 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2018-05
- Pages
- 3750-3757
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2962746398
- Cited by
- 441
- References
- 33
- FWCI
- 101.87
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Zeng, A., Song, S., Yu, K.-T., Donlon, E., Hogan, F. R., Bauza, M., Ma, D., Taylor, O., Liu, M., Romo, E., Fazeli, N., Alet, F., Dafle, N. C., Holladay, R., Morena, I., Qu Nair, P., Green, D., Taylor, I., Liu, W., . . . Rodriguez, A. (2018). Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching. 2018 IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/icra.2018.8461044
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