Vision-Based Efficient Robotic Manipulation with a Dual-Streaming Compact Convolutional Transformer.
Sensors (Basel)
; 23(1)2023 Jan 03.
Article
en En
| MEDLINE
| ID: mdl-36617113
ABSTRACT
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.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Robótica
/
Procedimientos Quirúrgicos Robotizados
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Sensors (Basel)
Año:
2023
Tipo del documento:
Article
País de afiliación:
China