Learning Stable Task Sequences from Demonstration with Linear Parameter Varying Systems and Hidden Markov Models
Learning Stable Task Sequences from Demonstration with Linear Parameter Varying Systems and Hidden Markov Models
复制标题
从线性参数变化系统和隐马尔可夫模型的演示中学习稳定的任务序列
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
A. Billard
中科院分区：
文献类型：
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作者：
J. Medina;A. Billard
从演示中获取多个任务的问题通常分为两个顺序过程：（1）不同子目标/子任务的分割或识别，以及（2）为每个子任务参数化控制策略的单独学习过程。因此，分割标准通常忽略控制策略的特征，而是依赖于简化的模型。本文的目的是一个单一的模型能够学习序列的复杂的时间无关的控制策略，提供强大的和稳定的行为。为此，我们首先提出了一种新的和有效的方法来学习面向目标的时间无关的运动模型，通过估计吸引子和动态行为的数据，保证使用线性参数变化（LPV）系统的稳定性。该方法使学习复杂的任务序列与隐马尔可夫模型（HALGORY），其中每个状态/子任务是由一个稳定的LPV系统，其中过渡是最有可能围绕相应的吸引子。我们研究了HMM-LPV模型的动态特性，并提出了一种保证任务序列稳定性的运动生成方法。我们验证我们的方法在两组演示人体运动。
The problem of acquiring multiple tasks from demonstration is typically divided in two sequential processes: (1) the segmentation or identification of different subgoals/subtasks and (2) a separate learning process that parameterizes a control policy for each subtask. As a result, segmentation criteria typically neglect the characteristics of control policies and rely instead on simplified models. This paper aims for a single model capable of learning sequences of complex time-independent control policies that provide robust and stable behavior. To this end, we first present a novel and efficient approach to learn goal-oriented timeindependent motion models by estimating both attractor and dynamic behavior from data guaranteeing stability using linear parameter varying (LPV) systems. This method enables learning complex task sequences with hidden Markov models (HMMs), where each state/subtask is given by a stable LPV system and where transitions are most likely around the corresponding attractor. We study the dynamics of the HMM-LPV model and propose a motion generation method that guarantees the stability of task sequences. We validate our approach in two sets of demonstrated human motions.
影响因子：
7.5
作者：
Christian Daniel;H. V. Hoof;Jan Peters;G. Neumann
通讯作者：
Christian Daniel;H. V. Hoof;Jan Peters;G. Neumann