Text2action: Generative adversarial synthesis from language to action
Ahn, Hyemin(Seoul National University)
Australia | ICRA 2018
2018-05-21 | 바로가기
Decoding, Robots, Gallium_nitride
Cited by 24
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2018 IEEE International Conference on Robotics and Automation (ICRA)
Date of Conference: 21-25 May 2018
Hyemin Ahn, Timothy Ha, Yunho Choi, Hwiyeon Yoo, Songhwai Oh
Department of Electrical and Computer Engineering and ASRI, Seoul National Uni-versity
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.
In this paper, we have proposed a generative model based on the Seq2seq model  and generative adversarial network (GAN) , for enabling a robot to execute various actions corresponding to an input language deion. It is interesting to note that our generative model, which is different from other existing related works in terms of utilizing the advantages of the GAN, is able to generate diverse behaviors when the input random vector sequence changes. In addition, results show that our network can generate an action sequence that is more dynamic and closer to the actual data than the network presented presented in . The proposed generative model, which understands the relationship between the human language and the action, generates an action corresponding to the input language. We believe that the proposed method can make actions by robots more understandable to their users.
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