Teaching a Robot to Grasp Real Fish by Imitation Learning from a Human Supervisor in Virtual Reality
| Autor: | Annette Stahl, Elling Ruud Øye, Jonatan S. Dyrstad, John Reidar Mathiassen |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Occupancy grid mapping Supervisor Computer science GRASP 02 engineering and technology Virtual reality Convolutional neural network Domain (software engineering) 020901 industrial engineering & automation Human–computer interaction Grippers 0202 electrical engineering electronic engineering information engineering Robot 020201 artificial intelligence & image processing Set (psychology) |
| Zdroj: | IEEE International Conference on Intelligent Robots and Systems. Proceedings IROS |
| Popis: | We teach a real robot to grasp real fish, by training a virtual robot exclusively in virtual reality. Our approach implements robot imitation learning from a human supervisor in virtual reality. A deep 3D convolutional neural network computes grasps from a 3D occupancy grid obtained from depth imaging at multiple viewpoints. In virtual reality, a human supervisor can easily and intuitively demonstrate examples of how to grasp an object, such as a fish. From a few dozen of these demonstrations, we use domain randomization to generate a large synthetic training data set consisting of 100 000 example grasps of fish. Using this data set for training purposes, the network is able to guide a real robot and gripper to grasp real fish with good success rates. The newly proposed domain randomization approach constitutes the first step in how to efficiently perform robot imitation learning from a human supervisor in virtual reality in a way that transfers well to the real world. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Databáze: | OpenAIRE |
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