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Predicting against the Flow: Boosting Source Localization by Means of Field Belief Modeling using Upstream Source Proximity

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Predicting against the Flow: Boosting Source Localization by...

IEEE International Conference on robotics and Automation (ICRA)

作者： Finn L Busch Nathalie Bauschmann Sami Haddadin Robert Seifried Daniel A Duecker Institute of Mechanics and Ocean Engineering Hamburg University of Technology Germany Division of Robotics Perception and Learning (RPL) KTH Royal Institute of Technology Sweden Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich (TUM) Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Time-effective and accurate source localization with mobile robots is crucial in safety-critical scenarios, e.g. leakage detection. This becomes particular challenging in realistic cluttered scenarios, i.e. in the presence of complex current flows or wind. Traditional methods often fall short due to simplifications or limited onboard *** propose to combine source localization with a Gaussian Markov Random Field (GMRF). This allows to improve source localization hypotheses by building on the GMRF’s concentration and flow field belief that are continuously updated by gathered measurements. We introduce the upstream source proximity (USP) as a natural metric that exploits the joint knowledge represented in the field belief’s concentration and flow field, i.e. predicting sources upstream. As a result, our method yields a computationally efficient source localization and field belief module providing substantially more stable gradients than conventional concentration gradient-based *** demonstrate the suitability of our approach in a series of numerical experiments covering complex source location scenarios. With regard to computational requirements, the method achieves update rates of 10Hz on a RaspberryPi4B.

关键词： Location awareness Measurement Wind Computational modeling Position measurement Predictive models Probabilistic logic

QueryAdapter: Rapid Adaptation of Vision-Language Models in Response to Natural Language Queries

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arXiv 2025年

作者： Chapman, Nicolas Harvey Dayoub, Feras Browne, Will Lehnert, Christopher School of Electrical Engineering and Robotics Queensland University of Technology Brisbane Australia School of Computer Science Australian Institute of Machine Learning University of Adelaide Adelaide Australia

A domain shift exists between the large-scale, internet data used to train a Vision-Language Model (VLM) and the raw image streams collected by a robot. Existing adaptation strategies require the definition of a closed-set of classes, which is impractical for a robot that must respond to diverse natural language queries. In response, we present QueryAdapter;a novel framework for rapidly adapting a pre-trained VLM in response to a natural language query. QueryAdapter leverages unlabelled data collected during previous deployments to align VLM features with semantic classes related to the query. By optimising learnable prompt tokens and actively selecting objects for training, an adapted model can be produced in a matter of minutes. We also explore how objects unrelated to the query should be dealt with when using real-world data for adaptation. In turn, we propose the use of object captions as negative class labels, helping to produce better calibrated confidence scores during adaptation. Extensive experiments on ScanNet++ demonstrate that QueryAdapter significantly enhances object retrieval performance compared to state-of-the-art unsupervised VLM adapters and 3D scene graph methods. Furthermore, the approach exhibits robust generalization to abstract affordance queries and other datasets, such as Ego4D. Copyright © 2025, The Authors. All rights reserved.

关键词： Structured Query Language

CONFORMALIZED INTERACTIVE IMITATION learning: HANDLING EXPERT SHIFT & INTERMITTENT FEEDBACK

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arXiv 2024年

作者： Zhao, Michelle Simmons, Reid Admoni, Henny Ramdas, Aaditya Bajcsy, Andrea Robotics Institute School of Computer Science Carnegie Mellon University United States Departments of Statistics and Machine Learning Carnegie Mellon University United States

In interactive imitation learning (IL), uncertainty quantification offers a way for the learner (i.e. robot) to contend with distribution shifts encountered during deployment by actively seeking additional feedback from an expert (i.e. human) online. Prior works use mechanisms like ensemble disagreement or Monte Carlo dropout to quantify when black-box IL policies are uncertain;however, these approaches can lead to overconfident estimates when faced with deployment-time distribution shifts. Instead, we contend that we need uncertainty quantification algorithms that can leverage the expert human feedback received during deployment time to adapt the robot’s uncertainty online. To tackle this, we draw upon online conformal prediction, a distribution-free method for constructing prediction intervals online given a stream of ground-truth labels. Human labels, however, are intermittent in the interactive IL setting. Thus, from the conformal prediction side, we introduce a novel uncertainty quantification algorithm called intermittent quantile tracking (IQT) that leverages a probabilistic model of intermittent labels, maintains asymptotic coverage guarantees, and empirically achieves desired coverage levels. From the interactive IL side, we develop ConformalDAgger, a new approach wherein the robot uses prediction intervals calibrated by IQT as a reliable measure of deployment-time uncertainty to actively query for more expert feedback. We compare ConformalDAgger to prior uncertainty-aware DAgger methods in scenarios where the distribution shift is (and isn’t) present because of changes in the expert’s policy. We find that in simulated and hardware deployments on a 7DOF robotic manipulator, ConformalDAgger detects high uncertainty when the expert shifts and increases the number of interventions compared to baselines, allowing the robot to more quickly learn the new behavior. Project page at ***/conformalized-interactive-il/. © 2024, CC BY.

关键词： Intelligent systems

Efficient LiDAR Bundle Adjustment for Multi-Scan Alignment Utilizing Continuous-Time Trajectories

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arXiv 2024年

作者： Wiesmann, Louis Marks, Elias Gupta, Saurabh Guadagnino, Tiziano Behley, Jens Stachniss, Cyrill The Center for Robotics University of Bonn Germany The Department of Engineering Science The University of Oxford United Kingdom The Lamarr Institute for Machine Learning and Artificial Intelligence Germany

Constructing precise global maps is a key task in robotics and is required for localization, surveying, monitoring, or constructing digital twins. To build accurate maps, data from mobile 3D LiDAR sensors is often used. Mapping requires correctly aligning the individual point clouds to each other to obtain a globally consistent map. In this paper, we investigate the problem of multi-scan alignment to obtain globally consistent point cloud maps. We propose a 3D LiDAR bundle adjustment approach to solve the global alignment problem and jointly optimize the available data. Utilizing a continuous-time trajectory allows us to consider the ego-motion of the LiDAR scanner while recording a single scan directly in the least squares adjustment. Furthermore, pruning the search space of correspondences and utilizing out-of-core circular buffer enables our approach to align thousands of point clouds efficiently. We successfully align point clouds recorded with a handheld LiDAR, as well as ones mounted on a vehicle, and are able to perform multi-session alignment. Copyright © 2024, The Authors. All rights reserved.

关键词： Continuous time systems

Temperature-Aware Memory Mapping and Active Cooling of Neural Processing Units

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Temperature-Aware Memory Mapping and Active Cooling of Neura...

International Symposium on Low Power Electronics and Design (ISLPED)

作者： Vahidreza Moghaddas Hammam Kattan Tim Bücher Mikail Yayla Jian-Jia Chen Hussam Amrouch TU Dortmund University University of Stuttgart Lamarr Institute for Machine Learning and Artificial Intelligences AI Processor Design Technical University of Munich Munich Institute of Robotics and Machine Intelligence

Neural processing units (NPUs) have become indispensable for meeting the high computational demands of deep neural networks (DNNs). They provide a very efficient solution, thanks to having a huge MAC array that enables massive parallelism. Nevertheless, such an architecture exhibits excessive on-chip power densities leading to a localized hot-spot that seriously heats its surroundings. This work demonstrates how the on-chip temperatures induced by the MAC array create a spatial thermal gradient through the on-chip SRAM memory. This makes the memory regions sensitive to different error probabilities (Perror), leading to significant accuracy drops when DNNs are being executed. To surmount this challenge, we employ on-chip superlattice thermoelectric (TEC) cooling devices that effectively reduce the memory temperature. Although scaling the memory voltage makes SRAM cells more sensitive to errors, it significantly decreases the leakage power, which compensates for the power consumed by the incorporated TEC devices. Furthermore, operating the SRAM at a lower voltage and temperature substantially increases its lifetime because voltage and temperature are key stimuli of transistor aging. By running multi-physics simulations using commercial finite-element tools and SPICE simulations for the 14nm FinFET technology, we accurately derive the relation between the Perror in different memory regions and the corresponding cooling cost. We then propose a three-stage temperature-aware layer-wise memory mapping that exploits different degrees of the sensitivity of NN layers to errors towards maximizing the DNN accuracy while minimizing the cooling cost. Experimental results reveal that our method notably improves the DNN accuracy compared to existing temperature-oblivious memory mapping.

关键词：

LiDAR-Based Registration Against Georeferenced Models for Globally Consistent Allocentric Maps

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LiDAR-Based Registration Against Georeferenced Models for Gl...

IEEE International Workshop on Safety, Security, and Rescue robotics (SSRR)

作者： Jan Quenzel Linus T. Mallwitz Benedikt T. Arnold Sven Behnke Autonomous Intelligent Systems Group Computer Science Institute VI - Intelligent Systems and Robotics Center for Robotics and Lamarr Institute for Machine Learning and Artificial Intelligence University of Bonn Germany Fraunhofer FIT Germany Fraunhofer IAIS Germany

ISBN: (数字)9798331510954

ISBN: (纸本)9798331510961

Modern unmanned aerial vehicles (UAVs) are irreplaceable in search and rescue (SAR) missions to obtain a situational overview or provide closeups without endangering personnel. However, UAVs heavily rely on global navigation satellite system (GNSS) for localization which works well in open spaces, but the precision drastically degrades in the vicinity of buildings. These inaccuracies hinder aggregation of diverse data from multiple sources in a unified georeferenced frame for SAR operators. In contrast, CityGML models provide approximate building shapes with accurate georeferenced poses. Besides, LiDAR works best in the vicinity of 3D structures. Hence, we refine coarse GNSS measurements by registering LiDAR maps against CityGML and digital elevation map (DEM) models as a prior for allocentric mapping. An intuitive plausibility score selects the best hypothesis based on occupancy using a 2D height map. Afterwards, we integrate the registration results in a continuous-time spline-based pose graph optimizer with LiDAR odometry and further sensing modalities to obtain globally consistent, georeferenced trajectories and maps. We evaluate the viability of our approach on multiple flights captured at two distinct testing sites. Our method successfully reduced GNSS offset errors from up-to 16 m to below 0.5 m on multiple flights. Furthermore, we obtain globally consistent maps w.r.t. prior 3D geospatial models.

关键词： Global navigation satellite system Solid modeling Laser radar Three-dimensional displays Buildings Data models Trajectory Geospatial analysis Splines (mathematics) Testing

TReR: A Lightweight Transformer Re-Ranking Approach for 3D LiDAR Place Recognition

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TReR: A Lightweight Transformer Re-Ranking Approach for 3D L...

International Conference on Intelligent Transportation

作者： Tiago Barros Luís Garrote Martin Aleksandrov Cristiano Premebida Urbano J. Nunes Department of Electrical and Computer Engineering University of Coimbra Institute of Systems and Robotics Portugal Dahlem Center for Machine Learning and Robotics Freie Universität Berlin Berlin

Autonomous driving systems often require reliable loop closure detection to guarantee reduced localization drift. Recently, 3D LiDAR-based localization methods have used retrieval-based place recognition to find revisited places efficiently. However, when deployed in challenging real-world scenarios, the place recognition models become more complex, which comes at the cost of high computational demand. This work tackles this problem from an information-retrieval perspective, adopting a first-retrieve-then-re-ranking paradigm, where an initial loop candidate ranking, generated from a 3D place recognition model, is re-ordered by a proposed lightweight transformer-based re-ranking approach (TReR). The proposed approach relies on global descriptors only, being agnostic to the place recognition model. The experimental evaluation, conducted on the KITTI Odometry dataset, where we compared TReR with s.o.t.a. re-ranking approaches such as αQE and SGV, indicate the robustness and efficiency when compared to αQE while offering a good trade-off between robustness and efficiency when compared to SGV.

关键词：

Balanced resonate-and-fire neurons 24

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Balanced resonate-and-fire neurons

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.

关键词：

LiDAR-based Registration against Georeferenced Models for Globally Consistent Allocentric Maps

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arXiv 2024年

作者： Quenzel, Jan Mallwitz, Linus T. Arnold, Benedikt T. Behnke, Sven Autonomous Intelligent Systems Group Computer Science Institute VI – Intelligent Systems and Robotics Germany Center for Robotics Lamarr Institute for Machine Learning and Artificial Intelligence University of Bonn Germany Fraunhofer FIT Germany Fraunhofer IAIS Germany

关键词： Continuous time systems