Robotic manipulation behavior should be robust to disturbances that violate
high-level task-structure. Such robustness can be achieved by constantly
monitoring the environment to observe the discrete high-level state of the
task. This is possible because different phases of a task are characterized by
different sensor patterns and by monitoring these patterns a robot can decide
which controllers to execute in the moment. This relaxes assumptions about the
temporal sequence of those controllers and makes behavior robust to unforeseen
disturbances. We implement this idea as probabilistic filter over discrete
states where each state is direcly associated with a controller. Based on this
framework we present a robotic system that is able to open a drawer and grasp
tennis balls from it in a surprisingly robust way.

通过不断监测环境来观察任务的离散高级状态，机器人控制器可以根据不同的传感器模式来决定执行哪些控制器，从而实现对干扰的鲁棒性。我们通过将该思想作为离散状态的概率滤波器来实现这一目标。基于这个框架，我们提出了一个机器人系统，可以惊人地稳定地打开抽屉并从中抓取网球。