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arXiv 2023年

作者： Lamanna, Leonardo Serafini, Luciano Faridghasemnia, Mohamadreza Saffiotti, Alessandro Saetti, Alessandro Gerevini, Alfonso Traverso, Paolo Fondazione Bruno Kessler Trento Italy Department of Information Engineering University of Brescia Italy Center for Applied Autonomous Sensor Systems University of Örebro Sweden

autonomous agents embedded in a physical environment need the ability to recognize objects and their properties from sensory data. Such a perceptual ability is often implemented by supervised machine learning models, which are pre-trained using a set of labelled data. In real-world, open-ended deployments, however, it is unrealistic to assume to have a pre-trained model for all possible environments. Therefore, agents need to dynamically learn/adapt/extend their perceptual abilities online, in an autonomous way, by exploring and interacting with the environment where they operate. This paper describes a way to do so, by exploiting symbolic planning. Specifically, we formalize the problem of automatically training a neural network to recognize object properties as a symbolic planning problem (using PDDL). We use planning techniques to produce a strategy for automating the training dataset creation and the learning process. Finally, we provide an experimental evaluation in both a simulated and a real environment, which shows that the proposed approach is able to successfully learn how to recognize new object properties. © 2023, CC BY.

关键词： autonomous agents

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Neural Network Approach for Shape-Based Euhedral Pyrite Identification in X-Ray Ct Data with Adversarial Unsupervised Domain Adaptation

SSRN

引用

SSRN 2023年

作者： Neelakantan, Suraj Norell, Jesper Hansson, Alexander Längkvist, Martin Loutfi, Amy Center for Applied Autonomous Sensor Systems Örebro University Örebro Sweden Orexplore AB Torshamngatan 30B Kista Stockholm164 40 Sweden

We explore an attenuation and shape-based identification of euhedral pyrites in high-resolution X-ray Computed Tomography (XCT) data using deep neural *** deal with the scarcity of annotated data we generate a complementary training set of synthetic images. To investigate and address the domain gap between the synthetic and XCT data, several deep learning models, with and without domain adaption, are trained and compared. We find that a model trained on a small set of human annotations, while displaying over-fitting, can rival the human annotators. The unsupervised domain adaptation approaches are successful in bridging the domain gap, which significantly improves their performance. A domain-adapted model, trained on a dataset that fuses synthetic and real data, is the overall best-performing model. This highlights the possibility of using synthetic datasets for the application of deep learning in mineralogy. © 2023, The Authors. All rights reserved.

关键词： Pyrites

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Discrimination between Cynomolgus Monkey (Macaca Fascicularis) and Humans using Machine Learning and Heart Rate Variability Indices

Discrimination between Cynomolgus Monkey (Macaca Fasciculari...

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2022 World Automation Congress, WAC 2022

作者： Kaneko, Itaru Hirahara, Daisuke Hayano, Junichiro Mozos, Oscar Martinez Yuda, Emi Center for Data-driven Science and Artificial Intelligence Tohoku University Sendai Japan Department of Health Sciences Graduate School of Medicine Tohoku University Sendai Japan Center for Applied Autonomous Sensor Systems ÖRebro University Örebro Sweden Nagoya City University Nagoya Japan

ISBN: (数字)9798888314449

ISBN: (纸本)9798888314449

In recent years, from the viewpoint of privacy protection policy, research on the personal identification of human bio-signals has been prominent. However, it is not well known whether indices obtained from biological time series data, such as ECGs, have personal identifiability, and it is not clear and can they be discriminated from animal ECGs. In this study, we visualized Heart Rate Variability (HRV) indices data of a cynomolgus monkey (Macaca Fascicularis) and a newborn using T-SNE, which is often used for data dimensionality compression in unsupervised learning, and UMAP, which is a new method. As a result, it was difficult to discriminate between cynomolgus monkey and newborn HRV indices. © 2022 TSI Enterprises.

关键词： Electrocardiograms

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Benchmarking Multi-Robot Coordination in Realistic, Unstructured Human-Shared Environments

Benchmarking Multi-Robot Coordination in Realistic, Unstruct...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Lukas Heuer Luigi Palmieri Anna Mannucci Sven Koenig Martin Magnusson Center for Applied Autonomous Sensor Systems (AASS) Örebro University Sweden Robert Bosch GmbH Corporate Research Stuttgart Germany Department of Computer Science University of Southern California Los Angeles United States of America

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Coordinating a fleet of robots in unstructured, human-shared environments is challenging. Human behavior is hard to predict, and its uncertainty impacts the performance of the robotic fleet. Various multi-robot planning and coordination algorithms have been proposed, including Multi-Agent Path Finding (MAPF) methods to precedence-based algorithms. However, it is still unclear how human presence impacts different coordination strategies in both simulated environments and the real world. With the goal of studying and further improving multi-robot planning capabilities in those settings, we propose a method to develop and benchmark different multi-robot coordination algorithms in realistic, unstructured and human-shared environments. To this end, we introduce a multi-robot benchmark framework that is based on state-of-the-art open-source navigation and simulation frameworks and can use different types of robots, environments and human motion models. We show a possible application of the benchmark framework with two different environments and three centralized coordination methods (two MAPF algorithms and a loosely-coupled coordination method based on precedence constraints). We evaluate each environment for different human densities to investigate its impact on each coordination method. We also present preliminary results that show how informing each coordination method about human presence can help the coordination method to find faster paths for the robots.

关键词： Uncertainty Navigation Robot kinematics Software algorithms Benchmark testing Prediction algorithms Software

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Learning to Propagate Interaction Effects for Modeling Deformable Linear Objects Dynamics

Learning to Propagate Interaction Effects for Modeling Defor...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yuxuan Yang Johannes A. Stork Todor Stoyanov Center for Applied Autonomous Sensor Systems (AASS) Örebro University Sweden

Modeling dynamics of deformable linear objects (DLOs), such as cables, hoses, sutures, and catheters, is an important and challenging problem for many robotic manipulation applications. In this paper, we propose the first method to model and learn full 3D dynamics of DLOs from data. Our approach is capable of capturing the complex twisting and bending dynamics of DLOs and allows local effects to propagate globally. To this end, we adapt the interaction network (IN) dynamics learning method for capturing the interaction between neighboring segments in a DLO and augment it with a recurrent model for propagating interaction effects along the length of a DLO. For learning twisting and bending dynamics in 3D, we also introduce a new suitable representation of DLO segments and their relationships. Unlike the original IN method, our model learns to propagate the effects of local interaction between neighboring segments to each segment in the chain within a single time step, without the need for iterated propagation steps. Evaluation of our model with synthetic and newly collected real-world data shows better accuracy and generalization in short-term and long- term predictions than the current state of the art. We further integrate our learned model in a model predictive control scheme and use it to successfully control the shape of a DLO. Our implementation is available at https://***/ammlab–public/in–bilstm.

关键词： Deformable models Adaptation models Solid modeling Three-dimensional displays Shape Predictive models Bending

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Compositional Factorization of Visual Scenes with Convolutional Sparse Coding and Resonator Networks

Compositional Factorization of Visual Scenes with Convolutio...

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Neuro Inspired Computational Elements Conference (NICE)

作者： Christopher J. Kymn Sonia Mazelet Annabel Ng Denis Kleyko Bruno A. Olshausen Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley USA Université Paris-Saclay ENS Paris-Saclay Gif-sur-Yvette France Centre for Applied Autonomous Sensor Systems Örebro University Örebro Sweden Intelligent Systems Lab Research Institutes of Sweden Kista Sweden

ISBN: (数字)9798350390582

ISBN: (纸本)9798350390599

We propose a system for visual scene analysis and recognition based on encoding the sparse, latent feature-representation of an image into a high-dimensional vector that is subsequently factorized to parse scene content. The sparse feature representation is learned from image statistics via convolutional sparse coding, while scene parsing is performed by a resonator network [1]. The integration of sparse coding with the resonator network increases the capacity of distributed representations and reduces collisions in the combinatorial search space during factorization. We find that for this problem the resonator network is capable of fast and accurate vector factorization, and we develop a confidence-based metric that assists in tracking the convergence of the resonator network.

关键词： Convolutional codes Measurement Visualization Image coding Image recognition Image analysis Vectors

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Binding in hippocampal-entorhinal circuits enables compositionality in cognitive maps 38

Binding in hippocampal-entorhinal circuits enables compositi...

引用

38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Kymn, Christopher J. Mazelet, Sonia Thomas, Anthony Kleyko, Denis Frady, E. Paxon Sommer, Friedrich T. Olshausen, Bruno A. Redwood Center for Theoretical Neuroscience Helen Wills Neuroscience Institute UC Berkeley Berkeley United States Université Paris-Saclay ENS Paris-Saclay Gif-sur-Yvette France Department of Electrical and Computer Engineering UC Davis Davis United States Centre for Applied Autonomous Sensor Systems Örebro University Örebro Sweden Intelligent Systems Lab Research Institutes of Sweden Kista Sweden Intel Labs Santa Clara United States Herbert Wertheim School of Optometry & Vision Science UC Berkeley Berkeley United States

We propose a normative model for spatial representation in the hippocampal formation that combines optimality principles, such as maximizing coding range and spatial information per neuron, with an algebraic framework for computing in distributed representation. Spatial position is encoded in a residue number system, with individual residues represented by high-dimensional, complex-valued vectors. These are composed into a single vector representing position by a similarity-preserving, conjunctive vector-binding operation. Self-consistency between the representations of the overall position and of the individual residues is enforced by a modular attractor network whose modules correspond to the grid cell modules in entorhinal cortex. The vector binding operation can also associate different contexts to spatial representations, yielding a model for entorhinal cortex and hippocampus. We show that the model achieves normative desiderata including superlinear scaling of patterns with dimension, robust error correction, and hexagonal, carry-free encoding of spatial position. These properties in turn enable robust path integration and association with sensory inputs. More generally, the model formalizes how compositional computations could occur in the hippocampal formation and leads to testable experimental predictions. © 2024 Neural information processing systems foundation. All rights reserved.

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Compositional Factorization of Visual Scenes with Convolutional Sparse Coding and Resonator Networks

arXiv

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arXiv 2024年

作者： Kymn, Christopher J. Mazelet, Sonia Ng, Annabel Kleyko, Denis Olshausen, Bruno A. Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley United States Université Paris-Saclay ENS Paris-Saclay Gif-sur-Yvette France Centre for Applied Autonomous Sensor Systems Örebro University Örebro Sweden Intelligent Systems Lab Research Institutes of Sweden Kista Sweden

关键词： Vectors

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Fuzzy Tuned PID Controller for Envisioned Agricultural Manipulator

引用

International Journal of Automation and computing 2021年第4期18卷 568-580页

作者： Satyam Paul Ajay Arunachalam Davood Khodadad Henrik Andreasson Olena Rubanenko Department of Engineering Design and Mathematics University of the West of EnglandBristol BS161QYUK Centre for Applied Autonomous Sensor Systems(AASS) Orebro UniversityOrebro 70281Sweden Department of Applied Physics and Electronics Umea UniversityUmea 90187Sweden Regional Innovational Center for Electrical Engineering Faculty of Electrical EngineeringUniversity of West BohemiaPilsen 30100Czech Republic

The implementation of image-based phenotyping systems has become an important aspect of crop and plant science research which has shown tremendous growth over the years. Accurate determination of features using images requires stable imaging and very precise processing. By installing a camera on a mechanical arm driven by motor, the maintenance of accuracy and stability becomes non-trivial. As per the state-of-the-art, the issue of external camera shake incurred due to vibration is a great concern in capturing accurate images, which may be induced by the driving motor of the manipulator. So, there is a requirement for a stable active controller for sufficient vibration attenuation of the manipulator. However, there are very few reports in agricultural practices which use control algorithms. Although, many control strategies have been utilized to control the vibration in manipulators associated to various applications, no control strategy with validated stability has been provided to control the vibration in such envisioned agricultural manipulator with simple low-cost hardware devices with the compensation of non-linearities. So, in this work, the combination of proportional-integral-differential(PID) control with type-2 fuzzy logic(T2-F-PID) is implemented for vibration control. The validation of the controller stability using Lyapunov analysis is established. A torsional actuator(TA) is applied for mitigating torsional vibration, which is a new contribution in the area of agricultural manipulators. Also, to prove the effectiveness of the controller, the vibration attenuation results with T2-F-PID is compared with conventional PD/PID controllers, and a type-1 fuzzy PID(T1-F-PID) controller.

关键词： Proportional-integral-differential(PID)controller fuzzy logic precision agriculture vibration control stability analysis modular manipulator agricultural robot computer numerical control(CNC)farming

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Binding in hippocampal-entorhinal circuits enables compositionality in cognitive maps

arXiv

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arXiv 2024年

作者： Kymn, Christopher J. Mazelet, Sonia Thomas, Anthony Kleyko, Denis Frady, E. Paxon Sommer, Friedrich T. Olshausen, Bruno A. Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley United States Université Paris-Saclay ENS Paris-Saclay Gif-sur-Yvette France Centre for Applied Autonomous Sensor Systems Örebro University Örebro Sweden Intel Labs Santa Clara United States

关键词： Vectors

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