@InProceedings{shimizu:robsym:2020,
  author    = {Shimizu, Soya and Ayusawa, Ko and Venture, Gentiane},
  title     = {低次元特徴空間を利用した運動合成と逆最適制御への応用},
  booktitle = {Robotics Symposia},
  year      = {2020},
  pages     = {113--119},
  address   = {Hakodate, Japan},
  month     = {March 15-March 16},
  note      = {Remote Conference (@Zoom)},
  keywords  = {Inverse optimal control, Motion generation, Functional PCA, Motion synthesis, Humanoid robot},
  abstract  = {This study aims to generate a complex multiple joint motion and estimate coefficients of the cost functions easily in a low-dimensional feature space using Functional Principal
Component Analysis (Functional PCA). Applying Functional PCA, each motion data is expressed by a point in a space called FPC space, so it is able to synthesis various motion data with ease. In addition, calculating a distance between a given point and others, our method can estimate weights of the objective functions of the given data with low cost and time instead of Inverse Optimal Control (IOC). In this paper, we applied our method to multiple link motion data, which includes some cost functions, and confirm the accuracy and the calculation speed of the weight estimate.}
}