학술대회논문

실감교류인체감응솔루션연구단의 주요 학술대회 성과를 소개합니다.

Evaluating Movement skills from Extended Neural Complexity

Evaluating Movement skills from Extended Neural Complexity
국내/국제 국제 국가명 한국
학술대회명 International Conference on Systems, Man, and Cybernetics(SMC) 2012 개최연월 2012-10

저자 WooYoung Kwon, IlHong Suh, BumJae You, SangRok Oh
초록
For a robot to learn complex movement skills, programming by demonstration and/or learning by trial and error is necessary. Measuring the complexity of such movement skills is important to decide the appropriate learning model, and the required size of dataset and additional prior knowledge. To deal with measuring the complexity of movement skills for robots, we propose an information–theoretic complexity measure. By modeling proprioceptive as well as exteroceptive sensory data as a multivariate Gaussian distribution, movement skills can be modeled as a probabilistic model. Next, complexity of the movement skills is measured by using neural complexity. In addition to the original neural complexity measure, endogeneous changes in time of the movement skills are modeled by sampling in time and modeling as individual random variables. To evaluate our proposed complexity measure, several experiments are performed on real robotic movement skills.
키워드 Complexity measure, Neural complexity, Timesliced Neural Complexity, Movement skills

과제명 사용자 행동양식의 모방 학습 기반 휴먼 아바타의 창발적 복합행동 생성 기술
연구기관 한양대학교 연구책임자 서일홍