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

Developing STEM Identity of Nez Perce Students: Identifying Entry-Level Competencies for Forestry and Fire Management

The purpose of this study was to identify the competencies that are required for entry-level forestry and fire management technicians. The strategy is a part of a larger goal to develop the STEM identity of Nez Perce students through the integration of relevant competencies in middle and high school curriculums. The DACUM process was used. Through this groupware process, nine experts from the Nez Perce Natural Resources produced a competency profile consisting of 12 duties and 79 tasks, along with general knowledge and skills, attitudes, tools, and future trends. Findings indicate that the experts view relevant cultural competencies as central to the function of the job and not as mere enablers. This has implications for how content is integrated, taught, and assessed in schools

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

Layer-Wise Relevance Propagation for Explaining Deep Neural Network Decisions in MRI-Based Alzheimer's Disease Classification

Deep neural networks have led to state-of-the-art results in many medical imaging tasks including Alzheimer’s disease (AD) detection based on structural magnetic resonance imaging (MRI) data. However, the network decisions are often perceived as being highly non-transparent, making it difficult to apply these algorithms in clinical routine. In this study, we propose using layer-wise relevance propagation (LRP) to visualize convolutional neural network decisions for AD based on MRI data. Similarly to other visualization methods, LRP produces a heatmap in the input space indicating the importance/relevance of each voxel contributing to the final classification outcome. In contrast to susceptibility maps produced by guided backpropagation (“Which change in voxels would change the outcome most?”), the LRP method is able to directly highlight positive contributions to the network classification in the input space. In particular, we show that (1) the LRP method is very specific for individuals (“Why does this person have AD?”) with high inter-patient variability, (2) there is very little relevance for AD in healthy controls and (3) areas that exhibit a lot of relevance correlate well with what is known from literature. To quantify the latter, we compute size-corrected metrics of the summed relevance per brain area, e.g., relevance density or relevance gain. Although these metrics produce very individual “fingerprints” of relevance patterns for AD patients, a lot of importance is put on areas in the temporal lobe including the hippocampus. After discussing several limitations such as sensitivity toward the underlying model and computation parameters, we conclude that LRP might have a high potential to assist clinicians in explaining neural network decisions for diagnosing AD (and potentially other diseases) based on structural MRI data

Making Motions in a Language we do not Understand: The Apophaticism of Thomas Aquinas and Victor Preller

Victor Preller's Divine Science and the Science of God makes an unjustly neglected contribution to understanding the apophaticism of Thomas Aquinas and, by extension, the possibilities and constraints of theological discourse. Preller contends that, according to Thomas, God-talk can be meaningful though not intelligible. That is, by faith one can know that one's propositions refer to God; one cannot, however, know how they do so. The first part of the article explains the main inferences leading up to these conclusions. The second part attends to some key passages in Thomas' Summa Theologiae in order to substantiate Preller's interpretation. Spelling out these passages requires coming to grips with Aquinas' distinction between the ‘thing signified' (res significata) and ‘mode of signification' (modus significandi). Armed with this second stock of concepts, the argument doubles back on the conclusions already set out: building on Preller, I argue that Thomas distinguishes between meaning and intelligibility for semantic reasons, judgements about the practice of language which are bound up with certain other ontological judgements. Throughout the article, the virtues of this line of interpretation are compared and contrasted with the position laid out in Kevin Hector's article, ‘Apophaticism in Thomas Aquinas: A Reformulation and Recommendation', Scottish Journal of Theology 60/4 (2007), pp. 377-9

Oozing Matters: Infracycles of “Waste Management” and Emergent Naturecultures in Phnom Penh

The Cambodian city of Phnom Penh displays a unique recyclable waste collection system. This article follows the daily practices of waste pickers and the movements of recyclable waste through the city. The hereby examined recurrent daily interactions define the overall infrastructure of recyclable waste handling that can be described as infracycles: sociomaterial constellations through which the quotidian flows of persons, goods, tools, narratives and ideas are organized in a recurrent and circular manner, thereby functioning as an actual lived infrastructure. This infrastructure is lived out bottom-up, as waste pickers, depot owners, and others interrelate. As waste circulates through cycles, different sociomaterialities emerge, which shape the city. Keeping the city somewhat clean, waste pickers form material itineraries and direct flows that shape urban ecologies. In the same process, oozy materials leaking from infracycles also create new versions of the city in the form of urban naturecultures, which compete with other imaginaries and designs for Phnom Penh’s urban transformation