摘要
针对运动捕获系统获取的人体运动轨迹固定、难以实现仿人机器人关键姿势转换问题,提出了一种基于分层Option学习的仿人机器人关键姿势相似性转换方法。构建多级关键姿势树状结构,从关节相似差异、时刻整体相似差异、周期整体相似差异等角度描述了关键姿势差异,引入分层强化Option学习方法,建立关键姿势与Option行为集,由关键姿势差异的累计奖励将SMDP-Q方法逼近最优Option值函数,实现了关键姿势的转换。实验验证了方法的有效性。
Concerning the problem in which the fixed locomotion track captured from human movement can not be used in transformation between key postures for humanoid robot, a method of similar key posture transformation based on hierarchical Option for humanoid robot was proposed. The multi-level dendrogram of key postures was constructed and the difference of key postures was illustrated in respects of similar joint difference, moment total similar difference, period total similar difference. The hierarchical reinforcement Option learning was introduced, in which the sets of key postures and Option actions were constructed. SMDP-Q method tended to be the optimal Option function by the accumulative rewards of key posture difference and the transformations were realized. The experiments show the validity of the method.
出处
《计算机应用》
CSCD
北大核心
2013年第5期1301-1304,共4页
journal of Computer Applications
基金
国家自然科学基金资助项目(61272382)
广东省自然科学基金资助项目(8152500002000003
S2012010009963)
广东省高等学校科技创新项目(2012KJCX0077)
广东高校石化装备故障诊断与信息化控制工程中心项目(512009)
关键词
仿人机器人
分层强化学习
相似性
姿势
humanoid robot
hierarchical reinforcement learning
similarity
posture
