Vision-Based Efficient Robotic Manipulation with a Dual-Streaming Compact Convolutional Transformer
Learning from visual observation for efficient robotic manipulation is a hitherto significant challenge in Reinforcement Learning (RL). Although the collocation of RL policies and convolution neural network (CNN) visual encoder achieves high efficiency and success rate, the method general performanc...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/1/515 |
| _version_ | 1827617155596681216 |
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| author | Hao Guo Meichao Song Zhen Ding Chunzhi Yi Feng Jiang |
| author_facet | Hao Guo Meichao Song Zhen Ding Chunzhi Yi Feng Jiang |
| author_sort | Hao Guo |
| collection | DOAJ |
| description | Learning from visual observation for efficient robotic manipulation is a hitherto significant challenge in Reinforcement Learning (RL). Although the collocation of RL policies and convolution neural network (CNN) visual encoder achieves high efficiency and success rate, the method general performance for multi-tasks is still limited to the efficacy of the encoder. Meanwhile, the increasing cost of the encoder optimization for general performance could debilitate the efficiency advantage of the original policy. Building on the attention mechanism, we design a robotic manipulation method that significantly improves the policy general performance among multitasks with the lite Transformer based visual encoder, unsupervised learning, and data augmentation. The encoder of our method could achieve the performance of the original Transformer with much less data, ensuring efficiency in the training process and intensifying the general multi-task performances. Furthermore, we experimentally demonstrate that the master view outperforms the other alternative third-person views in the general robotic manipulation tasks when combining the third-person and egocentric views to assimilate global and local visual information. After extensively experimenting with the tasks from the OpenAI Gym Fetch environment, especially in the Push task, our method succeeds in 92% versus baselines that of 65%, 78% for the CNN encoder, 81% for the ViT encoder, and with fewer training steps. |
| first_indexed | 2024-03-09T09:40:57Z |
| format | Article |
| id | doaj.art-a53e10a41a844ab88c1c39fefc38aa27 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T09:40:57Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a53e10a41a844ab88c1c39fefc38aa272023-12-02T00:58:03ZengMDPI AGSensors1424-82202023-01-0123151510.3390/s23010515Vision-Based Efficient Robotic Manipulation with a Dual-Streaming Compact Convolutional TransformerHao Guo0Meichao Song1Zhen Ding2Chunzhi Yi3Feng Jiang4School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Medicine and Health, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, ChinaLearning from visual observation for efficient robotic manipulation is a hitherto significant challenge in Reinforcement Learning (RL). Although the collocation of RL policies and convolution neural network (CNN) visual encoder achieves high efficiency and success rate, the method general performance for multi-tasks is still limited to the efficacy of the encoder. Meanwhile, the increasing cost of the encoder optimization for general performance could debilitate the efficiency advantage of the original policy. Building on the attention mechanism, we design a robotic manipulation method that significantly improves the policy general performance among multitasks with the lite Transformer based visual encoder, unsupervised learning, and data augmentation. The encoder of our method could achieve the performance of the original Transformer with much less data, ensuring efficiency in the training process and intensifying the general multi-task performances. Furthermore, we experimentally demonstrate that the master view outperforms the other alternative third-person views in the general robotic manipulation tasks when combining the third-person and egocentric views to assimilate global and local visual information. After extensively experimenting with the tasks from the OpenAI Gym Fetch environment, especially in the Push task, our method succeeds in 92% versus baselines that of 65%, 78% for the CNN encoder, 81% for the ViT encoder, and with fewer training steps.https://www.mdpi.com/1424-8220/23/1/515bio-inspired design and control of robotsroboticsreinforcement learningvision transformer |
| spellingShingle | Hao Guo Meichao Song Zhen Ding Chunzhi Yi Feng Jiang Vision-Based Efficient Robotic Manipulation with a Dual-Streaming Compact Convolutional Transformer Sensors bio-inspired design and control of robots robotics reinforcement learning vision transformer |
| title | Vision-Based Efficient Robotic Manipulation with a Dual-Streaming Compact Convolutional Transformer |
| title_full | Vision-Based Efficient Robotic Manipulation with a Dual-Streaming Compact Convolutional Transformer |
| title_fullStr | Vision-Based Efficient Robotic Manipulation with a Dual-Streaming Compact Convolutional Transformer |
| title_full_unstemmed | Vision-Based Efficient Robotic Manipulation with a Dual-Streaming Compact Convolutional Transformer |
| title_short | Vision-Based Efficient Robotic Manipulation with a Dual-Streaming Compact Convolutional Transformer |
| title_sort | vision based efficient robotic manipulation with a dual streaming compact convolutional transformer |
| topic | bio-inspired design and control of robots robotics reinforcement learning vision transformer |
| url | https://www.mdpi.com/1424-8220/23/1/515 |
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