Discovering imitation strategies through categorization of multi-dimensional data
Discovering imitation strategies through categorization of multi-dimensional data
复制标题
通过多维数据分类发现模仿策略
DOI：
10.1109/iros.2003.1249229
复制
发表时间：
2003
期刊：
影响因子：
--
通讯作者：
S. Schaal
中科院分区：
文献类型：
--
作者：
A. Billard;Yann Epars;G. Cheng;S. Schaal
模仿的一个基本问题是确定“模仿什么”，即确定演示的许多特征中哪些与任务相关，哪些应该复制。模仿者遵循的策略可以被建模为一个递阶优化系统，它最小化了两个多维数据集之间的差异。我们考虑对操纵任务的模仿。为了对操作策略进行分类，我们对笛卡尔空间和联合空间中的数据进行了概率分析。在策略确定之后，我们确定了一个优化任务复制策略的通用度量。该模型成功地发现了6个不同操作任务的策略，并通过一个全身仿人机器人控制任务的复制。
An essential problem of imitation is that of determining "what to imitate", i.e. to determine which of the many features of the demonstration are relevant to the task and which should be reproduced. The strategy followed by the imitator can be modeled as a hierarchical optimization system, which minimizes the discrepancy between two multi-dimensional datasets. We consider imitation of a manipulation task. To classify across manipulation strategies, we apply a probabilistic analysis to data in Cartesian and joint spaces. We determine a general metric that optimizes the policy of task reproduction, following strategy determination. The model successfully discovers strategies in six different manipulation tasks and controls task reproduction by a full body humanoid robot.