| 研究課題/領域番号 |
20K11998
|
|---|
| 研究種目 |
基盤研究(C)
|
| 配分区分 | 基金 |
|---|
| 応募区分 | 一般 |
|---|
| 審査区分 |
小区分61040:ソフトコンピューティング関連
|
|---|
| 研究機関 | 早稲田大学 |
|---|
研究代表者 |
Parque Victor 早稲田大学, 理工学術院, 准教授(任期付) (50745221)
|
|---|
| 研究期間 (年度) |
2020-04-01 – 2024-03-31
|
| 研究課題ステータス |
交付 (2022年度)
|
| 配分額 *注記 |
4,420千円 (直接経費: 3,400千円、間接経費: 1,020千円)
2022年度: 520千円 (直接経費: 400千円、間接経費: 120千円)
2021年度: 1,950千円 (直接経費: 1,500千円、間接経費: 450千円)
2020年度: 1,950千円 (直接経費: 1,500千円、間接経費: 450千円)
|
|---|
| キーワード | graph learning / optimization / networks / design / robotics / planning / succinct representation / succint representation |
|---|
| 研究開始時の研究の概要 |
This research aims at developing the state of the art algorithms for learning graph structures by enumerative queries on the succinct combinatorial search space of graphs, and at developing its practical applications in graph architecture search and network planning problems in robotics.
|
| 研究実績の概要 |
The algorithmic foundations and applications of learning and optimization of graphs via enumerative queries in computer-aided design and robotics were proposed: (1) the generation of obstacle-avoiding paths in grid maps by enumerative encoding scheme, the bijection to tree structures, and its rigorous evaluations/comparisons with gradient-free heuristics; (2) the sampling of robot locomotion gaits by enumerative representation schemes and its through evaluations by gradient-free optimization heuristics, enabling the efficient sampling and generation of adaptive gaits for legged robots; (3) the representation of curvature in edges of graph-based membrane folding applications, allowing the efficient folding/unfolding of network-based structures; (4) the generation of robot manipulator trajectories by using graph-based representation and extreme learning schemes, enabling the planning of robot manipulator trajectories with utmost efficiency, in the order of milliseconds; (5) the learning/optimization of cable-driven robot mechanisms by graph-based representations and gradient-free optimization schemes, enabling the possibility to devise new configurations of cable-driven robot structures; and (6) the study on the visualization of resource distribution networks on the plane, enabling the possibility to design optimal networks interactively.
|
| 現在までの達成度 (区分) |
現在までの達成度 (区分)
1: 当初の計画以上に進展している
理由
The relevant algorithmic foundations and application benchmarks to represente, learn and optimize graph structures via enumerative queries for design and robotics problems were established.
|
| 今後の研究の推進方策 |
In 2023, the research on minimal enumerative representation of graphs and hypergraphs, and the applications of Learning Graphs with Enumerative Queries is to be conducted. The research on learning optimal bayesian and convolutional networks, and the further applications to computer-aided desin/optimization and robotics are to be conducted.
|
