Connecting Touch and Vision via Cross-Modal Prediction
© 2019 IEEE. Humans perceive the world using multi-modal sensory inputs such as vision, audition, and touch. In this work, we investigate the cross-modal connection between vision and touch. The main challenge in this cross-domain modeling task lies in the significant scale discrepancy between the t...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers (IEEE)
2021
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| Online Access: | https://hdl.handle.net/1721.1/137632 |
| _version_ | 1811081206375120896 |
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| author | Li, Yunzhu Zhu, Jun-Yan Tedrake, Russ Torralba, Antonio |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Li, Yunzhu Zhu, Jun-Yan Tedrake, Russ Torralba, Antonio |
| author_sort | Li, Yunzhu |
| collection | MIT |
| description | © 2019 IEEE. Humans perceive the world using multi-modal sensory inputs such as vision, audition, and touch. In this work, we investigate the cross-modal connection between vision and touch. The main challenge in this cross-domain modeling task lies in the significant scale discrepancy between the two: While our eyes perceive an entire visual scene at once, humans can only feel a small region of an object at any given moment. To connect vision and touch, we introduce new tasks of synthesizing plausible tactile signals from visual inputs as well as imagining how we interact with objects given tactile data as input. To accomplish our goals, we first equip robots with both visual and tactile sensors and collect a large-scale dataset of corresponding vision and tactile image sequences. To close the scale gap, we present a new conditional adversarial model that incorporates the scale and location information of the touch. Human perceptual studies demonstrate that our model can produce realistic visual images from tactile data and vice versa. Finally, we present both qualitative and quantitative experimental results regarding different system designs, as well as visualizing the learned representations of our model. |
| first_indexed | 2024-09-23T11:43:07Z |
| format | Article |
| id | mit-1721.1/137632 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:43:07Z |
| publishDate | 2021 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1376322023-04-14T15:56:04Z Connecting Touch and Vision via Cross-Modal Prediction Li, Yunzhu Zhu, Jun-Yan Tedrake, Russ Torralba, Antonio Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory © 2019 IEEE. Humans perceive the world using multi-modal sensory inputs such as vision, audition, and touch. In this work, we investigate the cross-modal connection between vision and touch. The main challenge in this cross-domain modeling task lies in the significant scale discrepancy between the two: While our eyes perceive an entire visual scene at once, humans can only feel a small region of an object at any given moment. To connect vision and touch, we introduce new tasks of synthesizing plausible tactile signals from visual inputs as well as imagining how we interact with objects given tactile data as input. To accomplish our goals, we first equip robots with both visual and tactile sensors and collect a large-scale dataset of corresponding vision and tactile image sequences. To close the scale gap, we present a new conditional adversarial model that incorporates the scale and location information of the touch. Human perceptual studies demonstrate that our model can produce realistic visual images from tactile data and vice versa. Finally, we present both qualitative and quantitative experimental results regarding different system designs, as well as visualizing the learned representations of our model. 2021-11-08T12:42:36Z 2021-11-08T12:42:36Z 2019-06 2021-01-27T17:48:43Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/137632 Li, Yunzhu, Zhu, Jun-Yan, Tedrake, Russ and Torralba, Antonio. 2019. "Connecting Touch and Vision via Cross-Modal Prediction." Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June. en 10.1109/CVPR.2019.01086 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) MIT web domain |
| spellingShingle | Li, Yunzhu Zhu, Jun-Yan Tedrake, Russ Torralba, Antonio Connecting Touch and Vision via Cross-Modal Prediction |
| title | Connecting Touch and Vision via Cross-Modal Prediction |
| title_full | Connecting Touch and Vision via Cross-Modal Prediction |
| title_fullStr | Connecting Touch and Vision via Cross-Modal Prediction |
| title_full_unstemmed | Connecting Touch and Vision via Cross-Modal Prediction |
| title_short | Connecting Touch and Vision via Cross-Modal Prediction |
| title_sort | connecting touch and vision via cross modal prediction |
| url | https://hdl.handle.net/1721.1/137632 |
| work_keys_str_mv | AT liyunzhu connectingtouchandvisionviacrossmodalprediction AT zhujunyan connectingtouchandvisionviacrossmodalprediction AT tedrakeruss connectingtouchandvisionviacrossmodalprediction AT torralbaantonio connectingtouchandvisionviacrossmodalprediction |