Research on Learning Graphs via Enumerative Queries and its Applications

提出了在计算机辅助设计和机器人学中利用枚举查询进行图的学习和优化的算法基础和应用：（1）利用枚举编码方案在栅格地图中生成避障路径，并将其映射到树结构上，与无梯度算法进行了严格的比较;（2）通过枚举表示方案对机器人运动步态进行采样，并通过无梯度优化算法对其进行评估，使得能够有效地采样和生成腿式机器人的自适应步态;（3）基于图的膜折叠应用的边缘中的曲率的表示，允许基于网络的结构的有效折叠/展开;（4）通过使用基于图的表示和极端学习方案来生成机器人操纵器轨迹，使得能够以毫秒级的最大效率规划机器人操纵器轨迹;（5）通过基于图形的表示和无梯度优化方案学习/优化缆索驱动机器人机构，使设计缆索驱动机器人结构的新配置成为可能;以及（6）研究平面上资源分配网络的可视化，使交互式设计最佳网络成为可能。

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.