Towards robot arm training in virtual reality using partial least squares regression
| Autor: | Benjamin Volmer, Bruce H. Thomas, Adrien Verhulst, Masahiko Inami, Maki Sugimoto, Adam Drogemuller |
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| Přispěvatelé: | Volmer, B, Verhulst, A, Inami, Masahiko, Drogemuller, A, Sugimoto, M, Thomas, BH, 26th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2019 Osaka, Japan 23-27 March 2019 |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Computer science human computer interaction (HCI) 02 engineering and technology Virtual reality Petri net centered computing Task (project management) Acceleration 020901 industrial engineering & automation robot arm Partial least squares regression 0202 electrical engineering electronic engineering information engineering Robot 020201 artificial intelligence & image processing human Robotic arm Simulation |
| Zdroj: | VR |
| Popis: | Robot assistance can reduce the user's workload of a task. However, the robot needs to be programmed or trained on how to assist the user. Virtual Reality (VR) can be used to train and validate the actions of the robot in a safer and cheaper environment. In this paper, we examine how a robotic arm can be trained using Coloured Petri Nets (CPN) and Partial Least Squares Regression (PLSR). Based upon these algorithms, we discuss the concept of using the user's acceleration and rotation as a sufficient means to train a robotic arm for a procedural task in VR. We present a work-in-progress system for training robotic limbs using VR as a cost effective and safe medium for experimentation. Additionally, we propose PLSR data that could be considered for training data analysis Refereed/Peer-reviewed |
| Databáze: | OpenAIRE |
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