The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Affordance detection of tool parts from geometric features
Abstract
As robots begin to collaborate with humans in everyday workspaces, they will need to understand the functions of tools and their parts. To cut an apple or hammer a nail, robots need to not just know the tool's name, but they must localize the tool's parts and identify their functions. Intuitively, the geometry of a part is closely related to its possible functions, or its affordances. Therefore, we propose two approaches for learning affordances from local shape and geometry primitives: 1) superpixel based hierarchical matching pursuit (S-HMP); and 2) structured random forests (SRF). Moreover, since a part can be used in many ways, we introduce a large RGB-Depth dataset where tool parts are labeled with multiple affordances and their relative rankings. With ranked affordances, we evaluate the proposed methods on 3 cluttered scenes and over 105 kitchen, workshop and garden tools, using ranked correlation and a weighted F-measure score [26]. Experimental results over sequences containing clutter, occlusions, and viewpoint changes show that the approaches return precise predictions that could be used by a robot. S-HMP achieves high accuracy but at a significant computational cost, while SRF provides slightly less accurate predictions but in real-time. Finally, we validate the effectiveness of our approaches on the Cornell Grasping Dataset [25] for detecting graspable regions, and achieve state-of-the-art performance.
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Cites
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Cited by
AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection
Do, T.-T., Nguyen, A., & Reid, I. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2018.8460902 · Cited by 305
Grounded Human-Object Interaction Hotspots From Video
Nagarajan, T., Feichtenhofer, C., & Grauman, K. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00878 · Cited by 147
3D AffordanceNet: A Benchmark for Visual Object Affordance Understanding
Deng, S., Xu, X., Wu, C., Chen, K., & Jia, K. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00182 · Cited by 124
Affordances from Human Videos as a Versatile Representation for Robotics
Bahl, S., Mendonca, R., Chen, L., Jain, U., & Pathak, D. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01324 · Cited by 119
“Given a scene, one can predict interactions using geometrybased rules for objects via 3D scene understanding [43, 78, 79, 134], estimating 3D physical attributes [8, 26, 41, 137] or through segmentation models trained on semantic interactions [102, 104], and thus require specialized datasets.”
Rearrangement: A Challenge for Embodied AI
Batra, D., Chang, A. X., Chernova, S., Davison, A. J., Deng, J., Koltun, V., Levine, S., Malik, J., Mordatch, I., Mottaghi, R., et al. (2020) · arXiv · doi:10.48550/arxiv.2011.01975 · Cited by 109
““is the bottle likely to break if it is dropped?” or “is the box light enough to be picked up or does it need to be pushed?”), or prediction of affordance heatmaps for objects [55].”
Background
Metadata
- Authors
- Myers, A., Teo, C. L., Fermuller, C., & Aloimonos, Y.
- Venue
- 2015 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2015-05
- Pages
- 1374-1381
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W1524405667
- Cited by
- 273
- References
- 39
- FWCI
- 25.80
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Myers, A., Teo, C. L., Fermuller, C., & Aloimonos, Y. (2015). Affordance detection of tool parts from geometric features. 2015 IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/icra.2015.7139369
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