Modeling articulated objects using active manipulation

The video illustrates a mobile, manipulation robot that interacts with various articulated objects, such as a fridge and a dishwasher, in a kitchen environment. During interaction, the robot learns their kinematic properties such as the rotation axis and the configuration space. Knowing the kinematic model of these objects improves the performance of the robot and enables motion planning. Service robots operating in domestic environments are typically faced with a variety of objects they have to deal with to fulfill their tasks. Some of these objects are articulated such as cabinet doors and drawers, or room and garage doors. The ability to deal with such articulated objects is relevant for service robots, as, for example, they need to open doors when navigating between rooms and to open cabinets to pick up objects in fetch-and-carry applications. We developed a complete probabilistic framework that enables robots to learn the kinematic models of articulated objects from observations of their motion. We combine parametric and nonparametric models consistently and utilize the advantages of both methods. As a result of our approach, a robot can robustly operate articulated objects in unstructured environments. All software is available open-source (including documentation and tutorials) on http://www.ros.org/wiki/articulation.