Self-Specialization of General Robot Plans Based on Experience
| Autor: | Michael Beetz, Gayane Kazhoyan, Sebastian Koralewski |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Control and Optimization Computer science Mechanical Engineering Supervised learning Biomedical Engineering 02 engineering and technology Plan (drawing) Computer Science Applications Data modeling Task (project management) Human-Computer Interaction 020901 industrial engineering & automation Artificial Intelligence Control and Systems Engineering Human–computer interaction Specialization (functional) 0202 electrical engineering electronic engineering information engineering Task analysis Systems architecture Robot 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition |
| Zdroj: | IEEE Robotics and Automation Letters. 4:3766-3773 |
| ISSN: | 2377-3774 |
| DOI: | 10.1109/lra.2019.2928771 |
| Popis: | For robots to work outside of laboratory settings, their plans should be applicable to a variety of environments, objects, task contexts, and hardware platforms. This requires general-purpose methods that are, at this moment, not sufficiently performant for real-world applications. We propose an approach to specialize such general plans through running them for specific tasks and thereby learning appropriate specializations from experience. We present a system architecture, which collects data during plan execution for making up supervised learning problems and utilizes learned models for specializing the plans in a closed loop. We demonstrate our approach by letting a PR2 robot specialize its general fetch and place plan, whereby learned results are automatically installed into the plan. We show that the specialized plan performs better than the original plan in a statistically significant sense. |
| Databáze: | OpenAIRE |
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