Continuous Modeling of Affordances in a Symbolic Knowledge Base
| Autor: | Kei Okada, Michael Beetz, Masayuki Inaba, Yuki Furuta, Asil Kaan Bozcuoglu |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Computer science business.industry 02 engineering and technology Computer Science::Robotics 020901 industrial engineering & automation Knowledge base Action (philosophy) Human–computer interaction 0202 electrical engineering electronic engineering information engineering Robot 020201 artificial intelligence & image processing Set (psychology) Affordance business |
| Zdroj: | IROS |
| DOI: | 10.1109/iros40897.2019.8968220 |
| Popis: | As robots start to execute complex manipulation tasks, they are expected to improve their skill set over time as humans do. A prominent approach to accomplish this is having robots to keep models of their actions based on their experiences in order to improve their action executions in the future. In this paper, we present such a methodology where robots start to execute some actions with random parameters and record their generic execution logs with semantic annotations in a symbolic knowledge base for robots. Using the data inside logs, multivariate Gaussian mixture models are fitted to the high-level action parameters for later executions. These affordance models are being updated whenever a new execution is carried on. In essence, robots can use these continuously-updated probabilistic model for improving their actions To prove the applicability we demonstrate opening-a-fridge-door experiments with a PR2 robot. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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