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arXiv 2024年

作者： García-Castellanos, Alejandro Marchetti, Giovanni Luca Kragic, Danica Scolamiero, Martina Amsterdam Machine Learning Lab University of Amsterdam Netherlands Department of Mathematics KTH Royal Institute of Technology Sweden Division of Robotics Perception and Learning KTH Royal Institute of Technology Sweden

Relative representations are an established approach to zero-shot model stitching, consisting of a non-trainable transformation of the latent space of a deep neural network. Based on insights of topological and geometric nature, we propose two improvements to relative representations. First, we introduce a normalization procedure in the relative transformation, resulting in invariance to non-isotropic rescalings and permutations. The latter coincides with the symmetries in parameter space induced by common activation functions. Second, we propose to deploy topological densification when fine-tuning relative representations, a topological regularization loss encouraging clustering within classes. We provide an empirical investigation on a natural language task, where both the proposed variations yield improved performance on zero-shot model stitching. © 2024, CC BY.

关键词： Topology

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Understanding the geometry of workspace obstacles in Motion Optimization

Understanding the geometry of workspace obstacles in Motion ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Nathan Ratliff Marc Toussaint Stefan Schaal Autonomous Motion Department Max Planck Institute for Intelligent Systems Tübingen Germany Machine Learning and Robotics Lab University of Stuttgart Germany

ISBN: (纸本)9781479969241

What is it that makes movement around obstacles hard? The answer seems clear: obstacles contort the geometry of the workspace and make it difficult to leverage what we consider easy and intuitive straight-line Cartesian geometry. But is Cartesian motion actually easy? It's certainly well-understood and has numerous applications. But beneath the details of linear algebra and pseudoinverses, lies a non-trivial Riemannian metric driving the solution. Cartesian motion is easy only because the pseudoinverse, our powerhouse tool, correctly represents how Euclidean workspace geometry pulls back into the configuration space. In light of that observation, it reasons that motion through a field of obstacles could be just as easy as long as we correctly account for how those obstacles warp the geometry of the space. This paper explores extending our geometric model of the robot beyond the notion of a Cartesian workspace space to fully model and leverage how geometry changes in the presence of obstacles. Intuitively, impenetrable obstacles form topological holes and geodesics curve around them accordingly. We formalize this intuition and develop a general motion optimization framework called Riemannian Motion Optimization (RieMO) to efficiently find motions using our geometric models. Our experiments demonstrate that, for many problems, obstacle avoidance can be much more natural when placed within the right geometric context.

关键词： Geometry Optimization Measurement Approximation methods Trajectory Robots Kinematics

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SESAME: Semantic editing of scenes by adding, manipulating or erasing objects

arXiv

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arXiv 2020年

作者： Ntavelis, Evangelos Romero, Andrés Kastanis, Iason van Gool, Luc Timofte, Radu Computer Vision Lab. ETH Zurich Switzerland Robotics and Machine Learning CSEM SA Switzerland PSI ESAT KU Leuven Belgium

Recent advances in image generation gave rise to powerful tools for semantic image editing. However, existing approaches can either operate on a single image or require an abundance of additional information. They are not capable of handling the complete set of editing operations, that is addition, manipulation or removal of semantic concepts. To address these limitations, we propose SESAME, a novel generator-discriminator pair for Semantic Editing of Scenes by Adding, Manipulating or Erasing objects. In our setup, the user provides the semantic labels of the areas to be edited and the generator synthesizes the corresponding pixels. In contrast to previous methods that employ a discriminator that trivially concatenates semantics and image as an input, the SESAME discriminator is composed of two input streams that independently process the image and its semantics, using the latter to manipulate the results of the former. We evaluate our model on a diverse set of datasets and report state-of-the-art performance on two tasks: (a) image manipulation and (b) image generation conditioned on semantic labels. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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learning to Control Redundant Musculoskeletal Systems with Neural Networks and SQP: Exploiting Muscle Properties

Learning to Control Redundant Musculoskeletal Systems with N...

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IEEE International Conference on robotics and Automation

作者： Danny Driess Heiko Zimmermann Simon Wolfen Dan Suissa Daniel Haeufle Daniel Hennes Marc Toussaint Syn Schmitt Machine Learning and Robotics Lab University of Stuttgart Germany Biomechanics and Biorobotics Group University of Stuttgart Germany Multi-Level Modeling in Motor Control and Rehabilitation Robotics University of Tübingen Germany

Modeling biomechanical musculoskeletal systems reveals that the mapping from muscle stimulations to movement dynamics is highly nonlinear and complex, which makes it difficult to control those systems with classical techniques. In this work, we not only investigate whether machine learning approaches are capable of learning a controller for such systems. We are especially interested in the question if the structure of the musculoskeletal apparatus exhibits properties that are favorable for the learning task. In particular, we consider learning a control policy from target positions to muscle stimulations. To account for the high actuator redundancy of biomechanical systems, our approach uses a learned forward model represented by a neural network and sequential quadratic programming to obtain the control policy, which also enables us to alternate the co-contraction level and hence allows to change the stiffness of the system and to include optimality criteria like small muscle stimulations. Experiments on both a simulated musculoskeletal model of a human arm and a real biomimetic muscle-driven robot show that our approach is able to learn an accurate controller despite high redundancy and nonlinearity, while retaining sample efficiency.

关键词： Muscles Joints Robots Biological system modeling Torque Biomechanics

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Addressing Bias in Fine-Grained Classification Datasets: A Strategy for Reliable Evaluation

Addressing Bias in Fine-Grained Classification Datasets: A S...

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Pattern Recognition Systems (ICPRS), International Conference on

作者： Stefan Wolf Jannik Koch Lars Sommer Jürgen Beyerer Vision and Fusion Lab Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany Fraunhofer IOSB Karlsruhe Germany Fraunhofer Center for Machine Learning

The high specificity of classes in fine-grained clas-sification tasks leads to a small number of images per class in the common research datasets. Thus, the intra-class variance, such as differences in vehicle colors for fine-grained vehicle classification, may not be properly represented. Consequently, there can be a heavy bias in regards to certain attributes, such as vehicle colors, leading to unintended information leakage to the classifier which can relate the vehicle color to a vehicle model. This, in turn, can result in misleading accuracy estimations as we show in this study. To address this issue, we propose a method to quantify the bias of a train-test split in regard to a specific attribute, providing a metric for the expressiveness of the results. To prevent the bias from resulting in misleading accuracy estimations, we apply a simple splitting scheme that separates the manifestations of the attribute. This split prevents the model from exploiting features which are unrelated to the actual task at hand, leading to more accurate estimations of the model's real-world performance and generalization ability. We demonstrate the effectiveness of our method by examining the vehicle color bias in fine-grained vehicle classification datasets. Our results show that the strong performance of current methods, which render this task as practically solved, is largely due to the exploitation of this bias. We can cope with it to some degree by transforming the images to grayscale, partly restoring the performance of the original split. However, the accuracies are still far lower than indicated by the original split. Additionally, we demonstrate that the original random train-test splits of datasets may show higher accuracies for poorlier generalizing methods, which renders experimentation to find better methods misleading. Therefore, better splitting schemes, such as our attribute-based splitting scheme, are required to obtain trustworthy results in experiments.

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SkyCam: A dataset of sky images and their irradiance values

arXiv

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arXiv 2021年

作者： Ntavelis, Evangelos Remund, Jan Schmid, Philipp Robotics & Deep Learning CSEM SA Computer Vision Lab ETH Zurich Alpnach Switzerland Energy & Climate Meteotest Bern Switzerland Industry 4.0 & Machine Learning CSEM SA Alpnach Switzerland

Recent advances in Computer Vision and Deep learning have enabled astonishing results in a variety of fields and applications. Motivated by this success, the SkyCam Dataset aims to enable image-based Deep learning solutions for short-term, precise prediction of solar radiation on a local level. For the span of a year, three different cameras in three topographically different locations in Switzerland are acquiring images of the sky every 10 seconds. Thirteen high resolution images with different exposure times are captured and used to create an additional HDR image. The images are paired with highly precise irradiance values gathered from a high-accuracy pyranometer. © 2021, CC BY-NC-SA.

关键词： Deep learning

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Balanced resonate-and-fire neurons 24

Balanced resonate-and-fire neurons

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Proceedings of the 41st International Conference on machine learning

作者： Saya Higuchi Sebastian Kairat Sander M. Bohté Sebastian Otte Adaptive AI Lab Institute of Robotics and Cognitive Systems University of Lübeck Germany Machine Learning Group Centrum Wiskunde & Informatica (CWI) Amsterdam The Netherlands

The resonate-and-fire (RF) neuron, introduced over two decades ago, is a simple, efficient, yet biologically plausible spiking neuron model, which can extract frequency patterns within the time domain due to its resonating membrane dynamics. However, previous RF formulations suffer from intrinsic shortcomings that limit effective learning and prevent exploiting the principled advantage of RF neurons. Here, we introduce the balanced RF (BRF) neuron, which alleviates some of the intrinsic limitations of vanilla RF neurons and demonstrates its effectiveness within recurrent spiking neural networks (RSNNs) on various sequence learning tasks. We show that networks of BRF neurons achieve overall higher task performance, produce only a fraction of the spikes, and require significantly fewer parameters as compared to modern RSNNs. Moreover, BRF-RSNN consistently provide much faster and more stable training convergence, even when bridging many hundreds of time steps during backpropagation through time (BPTT). These results underscore that our BRF-RSNN is a strong candidate for future large-scale RSNN architectures, further lines of research in SNN methodology, and more efficient hardware implementations.

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Target-Aware Implicit Mapping for Agricultural Crop Inspection

Target-Aware Implicit Mapping for Agricultural Crop Inspecti...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Shane Kelly Alessandro Riccardi Elias Marks Federico Magistri Tiziano Guadagnino Margarita Chli Cyrill Stachniss Vision for Robotics Lab ETH Zurich University of Bonn Lamarr Institute for Machine Learning and Artificial Intelligence Germany Department of Engineering Science University of Oxford UK

Crop inspection is a critical part of modern agricultural practices that helps farmers assess the current status of a field and then make crop management decisions. Current crop inspection methods are labour-intensive tasks, which makes them rather slow and expensive to apply. In this paper, we exploit recent advancements in implicit mapping to tackle the challenging context of agricultural environments to create dense maps of crop rows with high enough fidelity to be useful for automated crop inspection. Specifically, we map strawberry and sweet pepper crop rows using RGB images captured by a wheeled mobile field robot inside a greenhouse and then use this data to build 3D maps to document the development of plants and fruits. Our Target-Aware Implicit Mapping system (TAIM) uses a SLAM-based pose initialization strategy for robust pose convergence, an efficient information-guided training sample selection framework for faster loss reduction, and focuses on exploiting training samples for fruit regions of the scene, which are critical for crop inspection tasks, to create more accurate maps in less time.

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Robust Task and Motion Planning for Long-Horizon Architectural Construction Planning

Robust Task and Motion Planning for Long-Horizon Architectur...

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2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Valentin N. Hartmann Ozgur S. Oguz Danny Driess Marc Toussaint Achim Menges Machine Learning & Robotics Lab University of Stuttgart Germany Max Planck Institute for Intelligent Systems Germany Institute for Computational Design and Construction University of Stuttgart Germany

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

Integrating robotic systems in architectural and construction processes is of core interest to increase the efficiency of the building industry. Automated planning for such systems enables design analysis tools and facilitates faster design iteration cycles for designers and engineers. However, generic task-and-motion planning (TAMP) for long-horizon construction processes is beyond the capabilities of current approaches. In this paper, we develop a multi-agent TAMP framework for long horizon problems such as constructing a full-scale building. To this end we extend the Logic-Geometric Programming framework by sampling-based motion planning, a limited horizon approach, and a task-specific structural stability optimization that allow an effective decomposition of the task. We show that our framework is capable of constructing a large pavilion built from several hundred geometrically unique building elements from start to end autonomously.

关键词： Buildings Tools Stability analysis Planning Structural engineering Task analysis Optimization

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Balanced Pedestrian Attribute Recognition for Improved Attribute-based Person Retrieval

Balanced Pedestrian Attribute Recognition for Improved Attri...

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Pattern Recognition Systems (ICPRS), International Conference on

作者： Andreas Specker Jürgen Beyerer Fraunhofer IOSB Karlsruhe Germany Vision and Fusion Lab Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Fraunhofer Center for Machine Learning

Attribute-based person retrieval is a crucial task in surveillance systems since it enables the search for an individual if no image of the suspect is available. Searches can be based on soft biometric characteristics, understood as semantic attributes people typically use to describe each other. Examples are gender, age, or a description of the person's clothing and accessories. One possible solution is Pedestrian Attribute Recognition (PAR). Resulting attribute predictions are compared to the query attributes with the model's predictions. However, related literature indicates approaches tend to either perform well regarding the recognition of single attributes or achieve superior retrieval results at the expense of an increased number of false positives. This work aims to improve single-attribute recognition and attribute-based retrieval simultaneously by applying spatial projection and normalization modules. Spatial projection extracts features for different receptive fields to account for global as well as local attributes. Our PARNorm module normalizes attribute logits attribute-and instance-wise to enhance label-based PAR metrics without hurting instance-based performance. Furthermore, we propose a novel evaluation metric for attribute-based person retrieval that considers the degree of match between attribute queries and gallery images instead of using binary relevance labels. This allows for more realistic estimates of retrieval accuracy, which correlate strongly with the visual impression of the rankings. Our model outperforms the current state-of-the-art in generalization on the UPAR dataset and its challenge version.

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