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DC Field | Value | Language |
---|---|---|
dc.citation.conferencePlace | US | - |
dc.citation.conferencePlace | Brisbane, AUSTRALIA | - |
dc.citation.endPage | 5920 | - |
dc.citation.startPage | 5915 | - |
dc.citation.title | IEEE International Conference on Robotics and Automation | - |
dc.contributor.author | Ahn, Hyemin | - |
dc.contributor.author | Ha, Timothy | - |
dc.contributor.author | Choi, Yunho | - |
dc.contributor.author | Yoo, Hwiyeon | - |
dc.contributor.author | Oh, Songhwai | - |
dc.date.accessioned | 2023-12-19T15:49:30Z | - |
dc.date.available | 2023-12-19T15:49:30Z | - |
dc.date.created | 2022-06-08 | - |
dc.date.issued | 2018-05-21 | - |
dc.description.abstract | In this paper, we propose a generative model which learns the relationship between language and human action in order to generate a human action sequence given a sentence describing human behavior. The proposed generative model is a generative adversarial network (GAN), which is based on the sequence to sequence (SEQ2SEQ) model. Using the proposed generative network, we can synthesize various actions for a robot or a virtual agent using a text encoder recurrent neural network (RNN) and an action decoder RNN. The proposed generative network is trained from 29,770 pairs of actions and sentence annotations extracted from MSR-Video-to-Text (MSR-VTT), a large-scale video dataset. We demonstrate that the network can generate human-like actions which can be transferred to a Baxter robot, such that the robot performs an action based on a provided sentence. Results show that the proposed generative network correctly models the relationship between language and action and can generate a diverse set of actions from the same sentence. | - |
dc.identifier.bibliographicCitation | IEEE International Conference on Robotics and Automation, pp.5915 - 5920 | - |
dc.identifier.doi | 10.1109/ICRA.2018.8460608 | - |
dc.identifier.issn | 1050-4729 | - |
dc.identifier.scopusid | 2-s2.0-85055121346 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/58871 | - |
dc.identifier.url | https://dl.acm.org/doi/abs/10.1109/ICRA.2018.8460608 | - |
dc.identifier.wosid | 000446394504069 | - |
dc.language | 영어 | - |
dc.publisher | IEEE COMPUTER SOC | - |
dc.title | Text2Action: Generative Adversarial Synthesis from Language to Action | - |
dc.type | Conference Paper | - |
dc.date.conferenceDate | 2018-05-21 | - |
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