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41st International Conference on Machine Learning, ICML 2024

作者： Maene, Jaron Derkinderen, Vincent De Raedt, Luc KU Leuven Department of Computer Science Leuven Belgium Örebro University Centre for Applied Autonomous Sensor Systems Örebro Sweden

The limitations of purely neural learning have sparked an interest in probabilistic neurosymbolic models, which combine neural networks with probabilistic logical reasoning. As these neurosymbolic models are trained with gradient descent, we study the complexity of differentiating probabilistic reasoning. We prove that although approximating these gradients is intractable in general, it becomes tractable during training. Furthermore, we introduce WeightME, an unbiased gradient estimator based on model sampling. Under mild assumptions, WeightME approximates the gradient with probabilistic guarantees using a logarithmic number of calls to a SAT solver. Lastly, we evaluate the necessity of these guarantees on the gradient. Our experiments indicate that the existing biased approximations indeed struggle to optimize even when exact solving is still feasible. Copyright 2024 by the author(s)

关键词： Neural network models

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autonomous Navigation of Wheel Loaders using Task Decomposition and Reinforcement Learning 19

Autonomous Navigation of Wheel Loaders using Task Decomposit...

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19th IEEE International Conference on Automation Science and Engineering, CASE 2023

作者： Borngrund, Carl Bodin, Ulf Sandin, Fredrik Andreasson, Henrik Luleå University of Technology Eislab Luleå97187 Sweden Örebro University Centre for Applied Autonomous Sensor Systems 70182 Sweden

ISBN: (纸本)9798350320695

The short-loading cycle is a repetitive task performed in high quantities making it a good candidate for automation. Expert operators perform this task to upkeep high productivity while minimizing the environmental impact of using energy to propel the wheel loader. The need to balance productivity and environmental performance is essential for the sub-task of navigating the wheel loader between the pile of material and a dump truck receiving the material. This task is further complicated by behaviours of the wheel loader such as wheel slip depending on the tire-to-surface friction that is hard to model. Such uncertainties motivate the use of data-driven and adaptable approaches like reinforcement learning to automate navigation. In this paper, we examine the possibility to use reinforcement learning for the navigation sub-task. We focus on the process of developing a solution to the complete sub-task by decomposing it into two distinct steps and training two different agents to perform them separately. These steps are reversing from the pile and approaching the dump truck. The agents are trained in a simulation environment in which the wheel loader is modelled. Our results indicate that task decomposition can be helpful in performing the navigation compared to training a single agent for the entire sub-task. We present unsuccessful experiments using a single agent for the entire sub-task to illustrate difficulties associated with such an approach. A video of the results is available online11Video available at https://***/IZbgvHvSltI. © 2023 IEEE.

关键词： Wheels

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MSI-RPi: Affordable, Portable, and Modular Multispectral Imaging Prototype Suited to Operate in UV, Visible and Mid-Infrared Regions

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Journal of Mobile Multimedia 2022年第3期18卷 723-742页

作者： Arunachalam, Ajay Andreasson, Henrik MRO Lab. Centre for Applied Autonomous Sensor Systems (AASS) Örebro University Örebro Sweden

Digital plant inventory provides critical growth insights, given the associated data quality is good. Stable & high-quality image acquisition is critical for further examination. In this work, we showcase an affordable, portable, and modular spectral camera prototype, designed with open hardware’s and open-source software’s. The image sensors used were color, and infrared Pi micro-camera. The designed prototype presents the advantage as being low-cost and modular with respect to other general commercial market available spectral devices. The micro-size connected sensors make it a compact instrument that can be used for any general spectral acquisition purposes, along with the provision of custom selection of the bands, making the presented prototype design a Plug-nd-Play (PnP) setup that can be used in different wide application areas. The images acquired from our custom-built prototype were back-tested by performing image analysis and qualitative assessments. The image acquisition software, and processing algorithm has been programmed, which is bundled with our developed system. Further, an end-to-end automation script is integrated for the users to readily leverage the services on-demand. The design files, schematics, and all the related materials of the spectral block design is open-sourced with open-hardware license & is made available at https://***/ajayarunachalam/Multi-Spectral-Imaging-RaspberryPi-Design. The automated data acquisition scripts & the spectral image analysis done is made available at https://***/ajayarunachalam/SI-RPi. © 2022 River Publishers.

关键词： Image analysis Low-cost plant phenotype Open-hardware Raspberry pi Spectral imaging technology

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Towards Addressing Label Ambiguity in Sequential Emotional Responses Through Distribution Learning

Towards Addressing Label Ambiguity in Sequential Emotional R...

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International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII

作者： Eduardo Gutierrez Maestro Hadi Banaee Amy Loutfi Centre for Applied Autonomous Sensor Systems (AASS) Örebro University Sweden

ISBN: (数字)9798331516437

ISBN: (纸本)9798331516444

This work highlights the challenge of labeling data with single-label categories, as there may be ambiguity in the assigned labels. This ambiguity arises when a data sample, which can be influenced by previous affective events is labeled with a single-label category (known as priming). Label distribution learning (LDL) is proposed as an approach to contend with the ambiguity among labels. This approach has been relatively unexplored in the field of affective computing. In this work, an experiment is designed to explore the benefits of employing LDL using specifically the SEED and SEED-V datasets. In these datasets, different emotions are induced by exposing participants to a sequence of stimuli (videoclip watching). However, these datasets provide single labels, where each data point corresponds to one affective state or emotion. Due to the lack of label distributions within existing benchmarks, label enhancement serves as a preparatory step, whose goal is to compute label distributions from the feature space and single labels before training a label distribution learning model. Experimental results show that the LDL approach reduces confusion with respect to the emotion induced in the previous trial. Distribution learning is an approach that can help to further improve the prediction of affect, which to date remains a difficult and ambiguous concept to label.

关键词： Training Affective computing Computational modeling Predictive models Benchmark testing Data models Emotional responses Labeling

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Stress Lingers: Recognizing the Impact of Task Order on Design of Stress and Emotion Detection systems

Stress Lingers: Recognizing the Impact of Task Order on Desi...

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Data Science and Engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Eduardo Gutierrez Maestro Hadi Banaee Amy Loutfi Centre for Applied Autonomous Sensor Systems (AASS) Örebro University Sweden

This paper examines the significance of the priming effect in designing and developing models for recognizing of affective states. Using a public dataset, often considered a benchmark in automatic stress recognition, the significance of the priming effect is explicated. Two experimental setups confirm the importance of task ordering in this problem. The results demonstrate the statistical significance of the model’s confusion when the subject has previously experienced stress and illustrate the importance for the Affective Computing community to develop methods to mitigate the priming effect where the order of tasks impacts how data should be modelled.

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Learning the Approach During the Short-loading Cycle Using Reinforcement Learning

arXiv

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arXiv 2024年

作者： Borngrund, Carl Bodin, Ulf Andreasson, Henrik Sandin, Fredrik EISLAB Luleå University of Technology Luleå97187 Sweden The Centre for Applied Autonomous Sensor Systems Örebro University 70182 Sweden

The short-loading cycle is a repetitive task performed in high quantities, making it a great alternative for automation. In the short-loading cycle, an expert operator navigates towards a pile, fills the bucket with material, navigates to a dump truck, and dumps the material into the tipping body. The operator has to balance the productivity goal while minimising the fuel usage, to maximise the overall efficiency of the cycle. In addition, difficult interactions, such as the tyre-to-surface interaction further complicate the cycle. These types of hard-to-model interactions that can be difficult to address with rule-based systems, together with the efficiency requirements, motivate us to examine the potential of data-driven approaches. In this paper, the possibility of teaching an agent through reinforcement learning to approach a dump truck's tipping body and get in position to dump material in the tipping body is examined. The agent is trained in a 3D simulated environment to perform a simplified navigation task. The trained agent is directly transferred to a real vehicle, to perform the same task, with no additional training. The results indicate that the agent can successfully learn to navigate towards the dump truck with a limited amount of control signals in simulation and when transferred to a real vehicle, exhibits the correct behaviour. © 2024, CC BY.

关键词： Reinforcement learning

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KLAY: ACCELERATING ARITHMETIC CIRCUITS FOR NEUROSYMBOLIC AI

arXiv

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arXiv 2024年

作者： Maene, Jaron Derkinderen, Vincent Martires, Pedro Zuidberg Dos Department of Computer Science KU Leuven Leuven Belgium Centre for Applied Autonomous Sensor Systems Örebro University Örebro Sweden

A popular approach to neurosymbolic AI involves mapping logic formulas to arithmetic circuits (computation graphs consisting of sums and products) and passing the outputs of a neural network through these circuits. This approach enforces symbolic constraints onto a neural network in a principled and end-to-end differentiable way. Unfortunately, arithmetic circuits are challenging to run on modern tensor accelerators as they exhibit a high degree of irregular sparsity. To address this limitation, we introduce knowledge layers (KLAY), a new data structure to represent arithmetic circuits that can be efficiently parallelized on GPUs. Moreover, we contribute two algorithms used in the translation of traditional circuit representations to KLAY and a further algorithm that exploits parallelization opportunities during circuit evaluations. We empirically show that KLAY achieves speedups of multiple orders of magnitude over the state of the art, thereby paving the way towards scaling neurosymbolic AI to larger real-world applications. © 2024, CC BY.

关键词： Computer circuits

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Revisiting Distribution-Based Registration Methods

Revisiting Distribution-Based Registration Methods

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European Conference on Mobile Robots (ECMR)

作者： Himanshu Gupta Henrik Andreasson Martin Magnusson Simon Julier Achim J. Lilientha Centre for Applied Autonomous Sensor Systems (AASS) Orebro University Sweden Department of Computer Science University College London UK Perception for Intelligent Systems Technical University of Munich Germany and Centre for Applied Autonomous Sensor Systems (AASS) Orebro University Sweden

Normal Distribution Transformation (NDT) registration is a fast, learning-free point cloud registration algorithm that works well in diverse environments. It uses the compact NDT representation to represent point clouds or maps as a spatial probability function that models the occupancy likelihood in an environment. However, because of the grid discretization in NDT maps, the global minima of the registration cost function do not always correlate to ground truth, particularly for rotational alignment. In this study, we examined the NDT registration cost function in-depth. We evaluated three modifications (Student-t likelihood function, inflated covariance/heavily broadened likelihood curve, and overlapping grid cells) that aim to reduce the negative impact of discretization in classical NDT registration. The first NDT modification improves likelihood estimates for matching the distributions of small population sizes; the second modification reduces discretization artifacts by broadening the likelihood tails through covariance inflation; and the third modification achieves continuity by creating the NDT representations with overlapping grid cells (without increasing the total number of cells). We used the Pomerleau Dataset evaluation protocol for our experiments and found significant improvements compared to the classic NDT D2D registration approach (27.7% success rate) using the registration cost functions “heavily broadened likelihood NDT” (HBL- NDT) (34.7% success rate) and “over-lapping grid cells NDT” (OGC-NDT) (33.5% success rate). However, we could not observe a consistent improvement using the Student-t likelihood-based registration cost function (22.2% success rate) over the NDT P2D registration cost function (23.7% success rate). A comparative analysis with other state-of-art registration algorithms is also presented in this work. We found that HBL-NDT worked best for easy initial pose difficulties scenarios making it suitable for consecutive point cloud re

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Safe Reinforcement Learning via Probabilistic Logic Shields

arXiv

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arXiv 2023年

作者： Yang, Wen-Chi Marra, Giuseppe Rens, Gavin De Raedt, Luc Leuven AI KU Leuven Belgium Centre for Applied Autonomous Sensor Systems Örebro University Sweden

Safe Reinforcement learning (Safe RL) aims at learning optimal policies while staying safe. A popular solution to Safe RL is shielding, which uses a logical safety specification to prevent an RL agent from taking unsafe actions. However, traditional shielding techniques are difficult to integrate with continuous, end-to-end deep RL methods. To this end, we introduce Probabilistic Logic Policy Gradient (PLPG). PLPG is a model-based Safe RL technique that uses probabilistic logic programming to model logical safety constraints as differentiable functions. Therefore, PLPG can be seamlessly applied to any policy gradient algorithm while still providing the same convergence guarantees. In our experiments, we show that PLPG learns safer and more rewarding policies compared to other state-of-the-art shielding techniques. © 2023, CC BY.

关键词： Reinforcement learning

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autonomous Navigation of Wheel Loaders using Task Decomposition and Reinforcement Learning

Autonomous Navigation of Wheel Loaders using Task Decomposit...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Carl Borngrund Ulf Bodin Fredrik Sandin Henrik Andreasson EISLAB Luleå University of Technology Luleå Sweden Centre for Applied Autonomous Sensor Systems Örebro University Sweden

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