论文标题
PICO：原始模仿控制
PICO: Primitive Imitation for COntrol
论文作者
论文摘要
在这项工作中，我们探索了一个用于控制复杂系统的新型框架，称为Control Pico的原始模仿。该方法结合了模仿学习，任务分解和新颖的任务测序的想法，以从演示到新行为的概括。示范会自动分解为现有或缺失的子行为，这使框架可以识别新型行为，同时不复制现有行为。对新任务的概括是通过动态的行为原始人的动态混合来实现的。我们使用来自两个不同机器人平台的演示进行了评估。实验结果表明，PICO能够检测出新的行为原始行为并构建缺失的控制策略。
In this work, we explore a novel framework for control of complex systems called Primitive Imitation for Control PICO. The approach combines ideas from imitation learning, task decomposition, and novel task sequencing to generalize from demonstrations to new behaviors. Demonstrations are automatically decomposed into existing or missing sub-behaviors which allows the framework to identify novel behaviors while not duplicating existing behaviors. Generalization to new tasks is achieved through dynamic blending of behavior primitives. We evaluated the approach using demonstrations from two different robotic platforms. The experimental results show that PICO is able to detect the presence of a novel behavior primitive and build the missing control policy.
