Deep Correspondence Learning for Effective Robotic Teleoperation using Virtual Reality
| Autor: | Brian D. Ziebart, Tejas Sarma, George Maratos, Sanket Gaurav, Amey Barapatre, Zainab Al-Qurashi |
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| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Computer science business.industry Deep learning Virtual representation 02 engineering and technology Workspace Virtual reality 020901 industrial engineering & automation Operator (computer programming) Human–computer interaction Teleoperation 0202 electrical engineering electronic engineering information engineering Robot 020201 artificial intelligence & image processing Artificial intelligence business Humanoid robot |
| Zdroj: | Humanoids |
| Popis: | By projecting into a 3-D workspace, robotic teleoperation using virtual reality allows for a more intuitive method of control for the operator than using a 2-D view from the robot's visual sensors. This paper investigates a setup that places the teleoperator in a virtual representation of the robot's environment and develops a deep learning based architecture modeling the correspondence between the operator's movements in the virtual space and joint angles for a humanoid robot using data collected from a series of demonstrations. We evaluate the correspondence model's performance in a pick-and - place teleoperation experiment. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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