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iasTUMUNICH uploaded a new video
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iasTUMUNICH uploaded a new video
(1 month ago)
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iasTUMUNICH uploaded a new video
(4 months ago)
In this video, we present a generalized framework
for robustly operating ...
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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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iasTUMUNICH uploaded a new video
(4 months ago)
This video describes a novel object segmentation
approach for autonomous ...
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This video describes a novel object segmentation
approach for autonomous service robots acting in human living
environments. The proposed system allows a robot to effectively
segment textured objects in cluttered scenes by leveraging
its manipulation capabilities. In this interactive perception
approach, 2D-features are tracked while the robot actively
induces motions into a scene using its arm. Thereby, the
robot autonomously infers appropriate arm movements which
can effectively separate objects. The resulting tracked feature
trajectories are assigned to their corresponding object by using
a novel randomized clustering algorithm, which samples rigid
motion hypotheses for the a priori unknown number of scene
objects. We evaluated the approach on challenging scenes which
included occluded and reflective objects, as well as objects of
varying shapes and sizes.
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iasTUMUNICH uploaded a new video
(4 months ago)
Abstract—This video is associated with the paper titled: "Contracti...
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Abstract—This video is associated with the paper titled: "Contracting Curve Density Algorithm for Applications in Personal Robotics". The paper investigates an extended and optimized implementation of the state-of-the-art local curve fitting algorithm
named Contracting Curve Density (CCD) algorithm, originally developed by Hanek et al. In particular, we investigate its application in the field of personal robotics for the tasks such as the mobile manipulation which requires a segmentation of
objects in clutter and the tracking of them. The developed system mainly consists of the two functional parts, the CCD algorithm to fit the model curve in still images and the CCD tracker to track the model in the videos. We demonstrate algorithm's working
in various scenes using handheld camera and the cameras from the Personal Robot 2 (PR2). Achieved results show that the CCD algorithm achieves robustness and sub-pixel accuracy even in the presence of clutter, partial occlusion, and changes of illumination.
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