| Project/Area Number |
10650392
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Single-year Grants |
|---|
| Section | 一般 |
|---|
| Research Field |
System engineering
|
|---|
| Research Institution | Osaka University |
|---|
Principal Investigator |
MIYAZAKI Fumio Graduate School of Engineering Science Osaka University Professor, 大学院・基礎工学研究科, 教授 (20133142)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
JOO Sanwan Graduate School of Engineering Science Osaka University Research Associate, 基礎工学研究科, 助手 (70273604)
MACDORMAN Karl f. Graduate School of Engineering Science Osaka University Lecturer, 基礎工学研究科, 講師 (10294167)
MASUTANI Yasuhiro Graduate School of Engineering Science Osaka University Lecturer, 基礎工学研究科, 講師 (80219328)
NISHIKAWA Atsushi Graduate School of Engineering Science Osaka University Research Associate, 基礎工学研究科, 助手 (20283731)
|
|---|
| Project Period (FY) |
1998 – 1999
|
| Project Status |
Completed (Fiscal Year 1999)
|
| Budget Amount *help |
¥3,900,000 (Direct Cost: ¥3,900,000)
Fiscal Year 1999: ¥2,000,000 (Direct Cost: ¥2,000,000)
Fiscal Year 1998: ¥1,900,000 (Direct Cost: ¥1,900,000)
|
|---|
| Keywords | perception / behavior / intelligent robots / mappings / locally weighted regression / k-dimensional trees / environmental change / LWR(Locally Weigted Regression) / マッピング / 学習 / 状態予測 / K-D tree / 視覚サーボ系 / オプティカルフロー |
|---|
| Research Abstract |
Remarkable features of biological systems are the robustness and adaptability. Human movement is highly resilient to environmental perturbations. This suggests the presence of a learned sensorimotor mapping that compares visual, proprioceptive and motor signals to make open-loop adjustments.
The aim of this research project is to propose a new method of learning sensorimotor mappings that combines the KD-trees (k-dimensional trees) learning and LWR (locally weighted regression) learning. Key features of the proposed method are the ability to learn in "one shot" from an instance, to interpolate and blend across instances with sensitivity to local sampling density, to resist noise, and to learn a mapping piecewise (thus saving memory). The proposed method can be used to learn many kinds of sensorimotor mappings, such as stereoscopic hand-eye mapping for a robot that plays ping-pong and a sensorimotor mapping for navigating a mobile robot. The details of these examples are involved in this report.
|
