论文标题
“世界是它自己的最佳模型”：通过在线行为选择通过在线行为的稳健实际操纵
"The World Is Its Own Best Model": Robust Real-World Manipulation Through Online Behavior Selection
论文作者
论文摘要
机器人的操纵行为应该对违反高级任务结构的干扰应该是强大的。可以通过不断监视环境以观察任务的离散高度状态来实现这种鲁棒性。这是可能的，因为任务的不同阶段以不同的传感器模式为特征，并且通过监视机器人可以在当下执行哪个控制器的这些模式。这放松了关于这些控制器的时间序列的假设，并使行为稳健地对不可预见的干扰。我们将此思想作为概率过滤器实现，以在每个状态与控制器方向关联的离散状态上实现。基于此框架，我们提出了一个机器人系统，该系统能够以惊人的健壮方式打开抽屉并从中抓住网球。
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.
