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A Generalized Framework for Opening Doors and Drawers in Kitchen Environments

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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.

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