94 research outputs found
Learning to Singulate Objects using a Push Proposal Network
Learning to act in unstructured environments, such as cluttered piles of
objects, poses a substantial challenge for manipulation robots. We present a
novel neural network-based approach that separates unknown objects in clutter
by selecting favourable push actions. Our network is trained from data
collected through autonomous interaction of a PR2 robot with randomly organized
tabletop scenes. The model is designed to propose meaningful push actions based
on over-segmented RGB-D images. We evaluate our approach by singulating up to 8
unknown objects in clutter. We demonstrate that our method enables the robot to
perform the task with a high success rate and a low number of required push
actions. Our results based on real-world experiments show that our network is
able to generalize to novel objects of various sizes and shapes, as well as to
arbitrary object configurations. Videos of our experiments can be viewed at
http://robotpush.cs.uni-freiburg.deComment: International Symposium on Robotics Research (ISRR) 2017, videos:
http://robotpush.cs.uni-freiburg.d
Optimization Beyond the Convolution: Generalizing Spatial Relations with End-to-End Metric Learning
To operate intelligently in domestic environments, robots require the ability
to understand arbitrary spatial relations between objects and to generalize
them to objects of varying sizes and shapes. In this work, we present a novel
end-to-end approach to generalize spatial relations based on distance metric
learning. We train a neural network to transform 3D point clouds of objects to
a metric space that captures the similarity of the depicted spatial relations,
using only geometric models of the objects. Our approach employs gradient-based
optimization to compute object poses in order to imitate an arbitrary target
relation by reducing the distance to it under the learned metric. Our results
based on simulated and real-world experiments show that the proposed method
enables robots to generalize spatial relations to unknown objects over a
continuous spectrum.Comment: Accepted for publication at ICRA2018. Supplementary Video:
http://spatialrelations.cs.uni-freiburg.de
Deep Detection of People and their Mobility Aids for a Hospital Robot
Robots operating in populated environments encounter many different types of
people, some of whom might have an advanced need for cautious interaction,
because of physical impairments or their advanced age. Robots therefore need to
recognize such advanced demands to provide appropriate assistance, guidance or
other forms of support. In this paper, we propose a depth-based perception
pipeline that estimates the position and velocity of people in the environment
and categorizes them according to the mobility aids they use: pedestrian,
person in wheelchair, person in a wheelchair with a person pushing them, person
with crutches and person using a walker. We present a fast region proposal
method that feeds a Region-based Convolutional Network (Fast R-CNN). With this,
we speed up the object detection process by a factor of seven compared to a
dense sliding window approach. We furthermore propose a probabilistic position,
velocity and class estimator to smooth the CNN's detections and account for
occlusions and misclassifications. In addition, we introduce a new hospital
dataset with over 17,000 annotated RGB-D images. Extensive experiments confirm
that our pipeline successfully keeps track of people and their mobility aids,
even in challenging situations with multiple people from different categories
and frequent occlusions. Videos of our experiments and the dataset are
available at http://www2.informatik.uni-freiburg.de/~kollmitz/MobilityAidsComment: 7 pages, ECMR 2017, dataset and videos:
http://www2.informatik.uni-freiburg.de/~kollmitz/MobilityAids
Multimodal Deep Learning for Robust RGB-D Object Recognition
Robust object recognition is a crucial ingredient of many, if not all,
real-world robotics applications. This paper leverages recent progress on
Convolutional Neural Networks (CNNs) and proposes a novel RGB-D architecture
for object recognition. Our architecture is composed of two separate CNN
processing streams - one for each modality - which are consecutively combined
with a late fusion network. We focus on learning with imperfect sensor data, a
typical problem in real-world robotics tasks. For accurate learning, we
introduce a multi-stage training methodology and two crucial ingredients for
handling depth data with CNNs. The first, an effective encoding of depth
information for CNNs that enables learning without the need for large depth
datasets. The second, a data augmentation scheme for robust learning with depth
images by corrupting them with realistic noise patterns. We present
state-of-the-art results on the RGB-D object dataset and show recognition in
challenging RGB-D real-world noisy settings.Comment: Final version submitted to IROS'2015, results unchanged,
reformulation of some text passages in abstract and introductio
Spielend Lernen im Kindergarten. Neue Technologien im Einsatz
Dieses Kapitel behandelt den kindlichen Zugang zu neuen Technologien im Alltag. Da die Kinder von heute in einer Medienwelt aufwachsen und schon früh mit Medien und Medienprodukten in Kontakt kommen und diese auch nutzen, sind die vorschulischen Bildungsinstitutionen gefordert, aktive Medienarbeit zu leisten und dabei einen verantwortungsbewussten Umgang mit Medien zu fördern. In engem Zusammenhang mit dem Einsatz von neuen Technologien im Vorschulbereich steht die Medienbildung. Deren Förderung stellt schon in der frühen Mediennutzung eine Notwendigkeit dar und wird in diesem Kapitel anschaulich mit ihren wichtigsten Zieldimensionen beschrieben. Die Autorinnen und Autoren verweisen auf die spielerische Umsetzung medienpädagogischer Ziele und den damit verbundenen Einsatz von neuen Technologien. Die Förderung der Medienbildung lässt sich in der Kindergartenarbeit nicht losgelöst von anderen Bildungsschwerpunkten vollziehen und ist somit immer verknüpft mit unterschiedlichen Bereichen der kindlichen Entwicklung und Förderung. Beispiele aus der Praxis zeigen die sozialen, kommunikativen, lernmethodischen, spielerischen und bildenden Aspekte des Lernens und Lehrens mit neuen Technologien sowie die damit zusammenhängenden Herausforderungen und Hindernissen. (DIPF/Orig.
RANDOMIZED COMPARISON OF PREHOSPITAL INITIATED FACILITATED PCI VERSUS PRIMARY PCI IN ACUTE MYOCARDIAL INFARCTION WITH < 3 H AFTER SYMPTOM ONSET
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