2021 Fiscal Year Annual Research Report
自動的な知識の抽出に基づく動物行動学に基づくソーシャルロボットの行動モデルの獲得
| Project/Area Number |
19F19380
|
|---|
| Research Institution | Chuo University |
|---|
Principal Investigator |
新妻 実保子 中央大学, 理工学部, 教授 (10548118)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
VINCZE DAVID 中央大学, 理工学部, 外国人特別研究員
|
|---|
| Project Period (FY) |
2019-11-08 – 2022-03-31
|
| Keywords | reinforcement learning / Human-Robot Interaction / multi-agent systems / rule-base reduction / fuzzy rule interpolation / knowledge extraction / fuzzy control |
|---|
| Outline of Annual Research Achievements |
In this period the possibility of using multiple agents in the Fuzzy Rule Interpolation-based Reinforcement Learning (FRI-RL) and running them distributed in parallel was investigated. As the FRI-RL knowledge extraction method is inherently sequential, some sub-results can be different in the parallel version, but still providing a sufficient solution. This way the knowledge extraction can be performed much faster, therefore using the method on problems with a high dimension count becomes practical. Also, a possible bridging interface between the behaviour simulation model (Strange Situation Test (SST) realized with an FRI-based fuzzy automaton) and real physical robots have been partly designed and implemented. Experimenting with real physical robots is underway. Furthermore a suitable indoor localization system was constructed and adapted to the needs of the planned Human-Robot Interaction (HRI) scenario. This system is able to easily calibrate the indoor localization system’s virtual coordinate system to the real-world physical coordinate system, which makes our planned HRI experiments possible with real humans and mobile robots. A customized robot behaviour engine and a motion control system was developed to support the proposed artificial Strange Situation Test experiments.
|
| Research Progress Status |
令和3年度が最終年度であるため、記入しない。
|
| Strategy for Future Research Activity |
令和3年度が最終年度であるため、記入しない。
|
Research Products
(1 results)
