On learning, representing, and generalizing a task in a humanoid robot
On learning, representing, and generalizing a task in a humanoid robot
复制标题
DOI：
10.1109/tsmcb.2006.886952
复制
发表时间：
2007-04-01
影响因子：
--
通讯作者：
Billard, Aude
中科院分区：
文献类型：
--
作者：
Calinon, Sylvain;Guenter, Florent;Billard, Aude
我们提出了一个演示编程框架，用于一般地提取给定任务的相关特征，并解决将所获得的知识推广到不同上下文的问题。我们通过一系列实验验证了该架构，在这些实验中，人类演示者教授人形机器人简单的操作任务。通过首先使用主成分分析将运动数据投影到一般潜在空间，提出了基于概率的相关性估计。得到的信号使用高斯/伯努利混合分布（高斯混合模型/伯努利混合模型）进行编码。这提供了从机器人收集的不同模态之间的时空相关性的度量，可用于确定模仿性能的度量。然后使用高斯混合回归对轨迹进行广义化。最后，我们分析计算了优化模仿度量的轨迹，并利用该轨迹将技能推广到不同的环境中。
We present a programming-by-demonstration framework for generically extracting the relevant features of a given task and for addressing the problem of generalizing the acquired knowledge to different contexts. We validate the architecture through a series of experiments, in which a human demonstrator teaches a humanoid robot simple manipulatory tasks. A probability-based estimation of the relevance is suggested by first projecting the motion data onto a generic latent space using principal component analysis. The resulting signals are encoded using a mixture of Gaussian/Bernoulli distributions (Gaussian mixture model/Bernoulli mixture model). This provides a measure of the spatio-temporal correlations across the different modalities collected from the robot, which can be used to determine a metric of the imitation performance. The trajectories are then generalized using Gaussian mixture regression. Finally, we analytically compute the trajectory which optimizes the imitation metric and use this to generalize the skill to different contexts.