Deep Learning of Augmented Reality based Human Interactions for Automating a Robot Team
| Autor: | Menusha Munasinghe, Hasitha Wellaboda, Adhitha Dias, Ranga Rodrigo, Peshala Jayasekara, Yasod Rasanka |
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| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry Deep learning 02 engineering and technology 010501 environmental sciences Base (topology) 01 natural sciences Task (project management) Human–computer interaction 0202 electrical engineering electronic engineering information engineering Robot 020201 artificial intelligence & image processing Augmented reality Artificial intelligence business 0105 earth and related environmental sciences |
| Zdroj: | 2020 6th International Conference on Control, Automation and Robotics (ICCAR). |
| DOI: | 10.1109/iccar49639.2020.9108004 |
| Popis: | Getting a team of robots to achieve a relatively complex task using manual manipulation through augmented reality (AR) is interesting. However, the true potential of such an approach manifests when the system can learn from humans. We propose a system comprising a team of robots that performs a previously unseen task—a variant, to be specific—by learning from the sequences of actions taken by multiple human beings doing this task in various ways using deep learning (DL). The training inputs can be through actual manipulation of the team of robots using an augmented-reality tablet or through a simulator. Results indicate that the system is able to fulfill the specified variant of the task more than 80% of the time, inaccuracies mainly owing to unrealistic specifications of tasks. This opens up an avenue of training a team of robots, instead of crafting a rule base. |
| Databáze: | OpenAIRE |
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