A brain-inspired intention prediction model and its applications to humanoid robot
With the development of artificial intelligence and robotic technology in recent years, robots are gradually integrated into human daily life. Most of the human-robot interaction technologies currently applied to home service robots are programmed by the manufacturer first, and then instruct the use...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2022.1009237/full |
| _version_ | 1798028969681879040 |
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| author | Yuxuan Zhao Yi Zeng Yi Zeng Yi Zeng Yi Zeng |
| author_facet | Yuxuan Zhao Yi Zeng Yi Zeng Yi Zeng Yi Zeng |
| author_sort | Yuxuan Zhao |
| collection | DOAJ |
| description | With the development of artificial intelligence and robotic technology in recent years, robots are gradually integrated into human daily life. Most of the human-robot interaction technologies currently applied to home service robots are programmed by the manufacturer first, and then instruct the user to trigger the implementation through voice commands or gesture commands. Although these methods are simple and effective, they lack some flexibility, especially when the programming program is contrary to user habits, which will lead to a significant decline in user experience satisfaction. To make that robots can better serve human beings, adaptable, simple, and flexible human-robot interaction technology is essential. Based on the neural mechanism of reinforcement learning, we propose a brain-inspired intention prediction model to enable the robot to perform actions according to the user's intention. With the spike-timing-dependent plasticity (STDP) mechanisms and the simple feedback of right or wrong, the humanoid robot NAO could successfully predict the user's intentions in Human Intention Prediction Experiment and Trajectory Tracking Experiment. Compared with the traditional Q-learning method, the training times required by the proposed model are reduced by (N2 − N)/4, where N is the number of intentions. |
| first_indexed | 2024-04-11T19:17:54Z |
| format | Article |
| id | doaj.art-7cf4228777b04532979659a075578428 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-11T19:17:54Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-7cf4228777b04532979659a0755784282022-12-22T04:07:24ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2022-10-011610.3389/fnins.2022.10092371009237A brain-inspired intention prediction model and its applications to humanoid robotYuxuan Zhao0Yi Zeng1Yi Zeng2Yi Zeng3Yi Zeng4Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, ChinaResearch Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, ChinaCenter for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing, ChinaNational Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, ChinaSchool of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, ChinaWith the development of artificial intelligence and robotic technology in recent years, robots are gradually integrated into human daily life. Most of the human-robot interaction technologies currently applied to home service robots are programmed by the manufacturer first, and then instruct the user to trigger the implementation through voice commands or gesture commands. Although these methods are simple and effective, they lack some flexibility, especially when the programming program is contrary to user habits, which will lead to a significant decline in user experience satisfaction. To make that robots can better serve human beings, adaptable, simple, and flexible human-robot interaction technology is essential. Based on the neural mechanism of reinforcement learning, we propose a brain-inspired intention prediction model to enable the robot to perform actions according to the user's intention. With the spike-timing-dependent plasticity (STDP) mechanisms and the simple feedback of right or wrong, the humanoid robot NAO could successfully predict the user's intentions in Human Intention Prediction Experiment and Trajectory Tracking Experiment. Compared with the traditional Q-learning method, the training times required by the proposed model are reduced by (N2 − N)/4, where N is the number of intentions.https://www.frontiersin.org/articles/10.3389/fnins.2022.1009237/fullhuman-robot interactionintention predictionbrain-inspired modelspiking neural networkshumanoid robot |
| spellingShingle | Yuxuan Zhao Yi Zeng Yi Zeng Yi Zeng Yi Zeng A brain-inspired intention prediction model and its applications to humanoid robot Frontiers in Neuroscience human-robot interaction intention prediction brain-inspired model spiking neural networks humanoid robot |
| title | A brain-inspired intention prediction model and its applications to humanoid robot |
| title_full | A brain-inspired intention prediction model and its applications to humanoid robot |
| title_fullStr | A brain-inspired intention prediction model and its applications to humanoid robot |
| title_full_unstemmed | A brain-inspired intention prediction model and its applications to humanoid robot |
| title_short | A brain-inspired intention prediction model and its applications to humanoid robot |
| title_sort | brain inspired intention prediction model and its applications to humanoid robot |
| topic | human-robot interaction intention prediction brain-inspired model spiking neural networks humanoid robot |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2022.1009237/full |
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