"The World Is Its Own Best Model": Robust Real-World Manipulation Through Online Behavior Selection
This addresses robustness in robotic manipulation for real-world tasks, but it is incremental as it builds on existing controller-based frameworks.
The paper tackles the problem of making robotic manipulation robust to unforeseen disturbances by monitoring sensor patterns to decide which controllers to execute, resulting in a system that robustly opens a drawer and grasps tennis balls.
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.