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Robot learning system based on dynamic movement primitives and neural network
- Source :
- Neurocomputing. 451:205-214
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- In the process of Human-robot skill transfer, we require the robot to reproduce the trajectory of teacher and expect that the robot can generalize the learned trajectory. For the trajectory after generalization, we expect that the robot arm can accurately track. However, because the model of the robot can not be accurately obtained, some researchers have proposed using a neural network to approximate the unknown term. The parameters of the traditional RBF neural network are usually selected through the empirical and trial-and-error method, which maybe biased and inefficient. In addition, due to the end-effector of the mechanical arm trajectory will be constantly changing according to the needs of the task, when the neural network of compact set cannot contain the whole input vector, the neural network cannot achieve the ideal approximation effect. In this paper, the broad neural network is used to approximate the unknown terms of the robot. This method can reuse the motion controller that has been learned and complete other motions in the robot operating space without relearning its weight parameters. In this paper, the effectiveness of the proposed method is proved by the ultrasound scanning task.
- Subjects :
- 0209 industrial biotechnology
Artificial neural network
business.industry
Computer science
Generalization
Cognitive Neuroscience
Process (computing)
Motion controller
02 engineering and technology
Robot learning
Computer Science Applications
Computer Science::Robotics
020901 industrial engineering & automation
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
Trajectory
Robot
020201 artificial intelligence & image processing
Artificial intelligence
business
Robotic arm
Subjects
Details
- ISSN :
- 09252312 and 18728286
- Volume :
- 451
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi.dedup.....282fc26cd17b9894e2cd230107a753dd