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Published on Sep 19, 2011
In this video, we present a generalized framework for robustly operating previously unknown cabinets in kitchen environments. Our framework consists of the following four components: (1) a module for detecting both Lambertian and non-Lambertian (i.e. specular) handles, (2) a module for opening and closing novel cabinets using impedance control and for learning their kinematic models, (3) a module for storing and retrieving information about these objects in the map, and (4) a module for reliably operating cabinets of which the kinematic model is known. The presented work is the result of a collaboration of three PR2 beta sites. We rigorously evaluated our approach on 29 cabinets in five real kitchens located at our institutions. These kitchens contained 13 drawers, 12 doors, 2 refrigerators and 2 dishwashers. We evaluated the overall performance of detecting the handle of a novel cabinet, operating it and storing its model in a semantic map. We found that our approach was successful in 51.9% of all 104 trials. By carefully inspecting the failure cases, we found that the robot was often not strong enough (27.9%) to open the heavier cabinets. Less frequently, the robot failed to detect the handle (6.7%) or the gripper slipped off during operation (6.2%). Notably, opening known cabinets (of which the kinematic model had already been learned) always succeeded.With this work, we contribute a well-tested building block of open-source software for future robotic service applications.