Sensing Anomalies as Potential Hazards: Datasets and Benchmarks
Dalle Molle Institute for Artificial Intelligence (IDSIA)（达勒·莫尔人工智能研究所（IDSIA））；USI-SUPSI（瑞士意大利语区大学-瑞士南部应用科学与艺术大学）；Hovering Solutions Ltd（Hovering Solutions有限公司）
Dario Mantegazza, Carlos Redondo, Fran Espada, Luca M. Gambardella, Alessandro Giusti, Jérôme Guzzi
We consider the problem of detecting, in the visual sensing data stream of an autonomous mobile robot, semantic patterns that are unusual (i.e., anomalous) with respect to the robot's previous experience in similar environments. These anomalies might indicate unforeseen hazards and, in scenarios where failure is costly, can be used to trigger an avoidance behavior. We contribute three novel image-based datasets acquired in robot exploration scenarios, comprising a total of more than 200k labeled frames, spanning various types of anomalies. On these datasets, we study the performance of an anomaly detection approach based on autoencoders operating at different scales.
Hazards in Daily Life? Enabling Robots to Proactively Detect and Resolve Anomalies
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；The Australian National University（澳大利亚国立大学）；University of Technology Sydney（悉尼科技大学）；Northeastern University（东北大学）；University of Liverpool（利物浦大学）
Embodied Hazard Mitigation using Vision-Language Models for Autonomous Mobile Robots
Department of Mechanical and Aerospace Engineering, Tandon School of Engineering, New York University（机械与航空航天工程系，坦顿工程学院，纽约大学）；GenAuto.ai by General Autonomy Inc.（General Autonomy Inc. 的 GenAuto.ai）
Leveraging Time-Series Foundation Model for Subsurface Well Logs Prediction and Anomaly Detection
King Fahd University of Petroleum and Minerals（法赫德石油和矿产大学）；Center for Integrative Petroleum Research（综合石油研究中心）；College of Petroleum Engineering and Geosciences（石油工程与地球科学学院）
Unveiling the Invisible: Enhanced Detection and Analysis of Deteriorated Areas in Solar PV Modules Using Unsupervised Sensing Algorithms and 3D Augmented Reality
University of Biskra（比斯克拉大学）；University of Dubai（迪拜大学）；University of Technology Phra Nakhon（帕那空皇家理工大学）；College of Staten Island（史泰登岛学院）；Université Kasdi Merbah（卡斯迪·梅尔巴大学）
A harmonised dataset for Earth system foundation models
Politecnico di Milano, Department of Management, Economics and Industrial Engineering（米兰理工大学管理、经济与工业工程系）；RFF-CMCC European Institute on Economics and the Environment（资源未来研究所-欧洲经济与环境CMCC研究所）；CMCC Foundation - Euro-Mediterranean Center on Climate Change（CMCC基金会-欧洲地中海气候变化中心）
Distracted drivers are more likely to fail to anticipate hazards, which result in car accidents. Therefore, detecting anomalies in drivers' actions (i.e., any action deviating from normal driving) contains the utmost importance to reduce driver-related accidents. However, there are unbounded many anomalous actions that a driver can do while driving, which leads to an 'open set recognition' problem. Accordingly, instead of recognizing a set of anomalous actions that are commonly defined by previous dataset providers, in this work, we propose a contrastive learning approach to learn a metric to …
Unsupervised Learning for Detection of Rare Driving Scenarios
Professorship of Autonomous Vehicle Systems, TUM School of Engineering and Design, Technical University Munich（自主车辆系统教授职位，技术大学慕尼黑工程与设计学院）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）；CARIAD SE（CARIAD公司）
In this paper we address the problem of indoor localization using magnetic field data in two setups, when data is collected by (i) human-held mobile phone and (ii) by localization robots that perturb magnetic data with their own electromagnetic field. For the first setup, we revise the state of the art approaches and propose a novel extended pipeline to benefit from the presence of magnetic anomalies in indoor environment created by different ferromagnetic objects. We capture changes of the Earth's magnetic field due to indoor magnetic anomalies and transform them in multi-variate times series…
The detection of anomalous executions is valuable for reducing potential hazards in assistive manipulation. Multimodal sensory signals can be helpful for detecting a wide range of anomalies. However, the fusion of high-dimensional and heterogeneous modalities is a challenging problem. We introduce a long short-term memory based variational autoencoder (LSTM-VAE) that fuses signals and reconstructs their expected distribution. We also introduce an LSTM-VAE-based detector using a reconstruction-based anomaly score and a state-based threshold. For evaluations with 1,555 robot-assisted feeding exe…
A Semantic Observer Layer for Autonomous Vehicles: Pre-Deployment Feasibility Study of VLMs for Low-Latency Anomaly Detection
The City College of New York, Grove School of Engineering（纽约市立学院格罗夫工程学院）；Department of Mechanical and Aerospace Engineering, Tandon School of Engineering, New York University（纽约大学坦登工程学院机械与航空航天工程系）；Department of Electrical and Computer Engineering, Tandon School of Engineering, New York University（纽约大学坦登工程学院电气与计算机工程系）
This paper presents fast procedures for thermal infrared remote sensing in dark, GPS-denied environments, such as those found in industrial plants such as in High-Voltage Direct Current (HVDC) converter stations. These procedures are based on the combination of the depth estimation obtained from either a 1-Dimensional LIDAR laser or a 2-Dimensional Hokuyo laser or a 3D MultiSense SLB laser sensor and the visible and thermal cameras from a FLIR Duo R dual-sensor thermal camera. The combination of these sensors/cameras is suitable to be mounted on Unmanned Aerial Vehicles (UAVs) and/or robots in…
SlipSense: Multimodal Sensing for Online Slip Detection in Legged Robots
School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；Institute for Infocomm Research, Agency for Science Technology and Research（资讯通信研究院，科技研究局）
