Tootfinder

Building Forest Inventories with Autonomous Legged Robots -- System, Lessons, and Challenges Ahead
Mat\'ias Mattamala, Nived Chebrolu, Jonas Frey, Leonard Frei{\ss}muth, Haedam Oh, Benoit Casseau, Marco Hutter, Maurice Fallon
https://arxiv.org/abs/2506.20315

Building Forest Inventories with Autonomous Legged Robots -- System, Lessons, and Challenges Ahead
Legged robots are increasingly being adopted in industries such as oil, gas, mining, nuclear, and agriculture. However, new challenges exist when moving into natural, less-structured environments, such as forestry applications. This paper presents a prototype system for autonomous, under-canopy forest inventory with legged platforms. Motivated by the robustness and mobility of modern legged robots, we introduce a system architecture which enabled a quadruped platform to autonomously navigate an…

Somewhat unsettling --
https://www.theguardian.com/world/2025/jun/25/ukraine-russia-autonomous-drones-ai?CMP=Share_AndroidApp_Other

Somewhat unsettling --
https://www.theguardian.com/world/2025/jun/25/ukraine-russia-autonomous-drones-ai?CMP=Share_AndroidApp_Other

Autonomous Cyber Resilience via a Co-Evolutionary Arms Race within a Fortified Digital Twin Sandbox
Malikussaid, Sutiyo
https://arxiv.org/abs/2506.20102 h…

Autonomous Cyber Resilience via a Co-Evolutionary Arms Race within a Fortified Digital Twin Sandbox
The convergence of IT and OT has created hyper-connected ICS, exposing critical infrastructure to a new class of adaptive, intelligent adversaries that render static defenses obsolete. Existing security paradigms often fail to address a foundational "Trinity of Trust," comprising the fidelity of the system model, the integrity of synchronizing data, and the resilience of the analytical engine against sophisticated evasion. This paper introduces the ARC framework, a method for achieving analytic…

The Autonomous Data Language -- Concepts, Design and Formal Verification
Tom T. P. Franken, Thomas Neele, Jan Friso Groote
https://arxiv.org/abs/2506.19457

The Autonomous Data Language -- Concepts, Design and Formal Verification
Nowadays, the main advances in computational power are due to parallelism. However, most parallel languages have been designed with a focus on processors and threads. This makes dealing with data and memory in programs hard, which distances the implementation from its original algorithm. We propose a new paradigm for parallel programming, the data-autonomous paradigm, where computation is performed by autonomous data elements. Programs in this paradigm are focused on making the data collaborate…

Sources: Uber is in talks with Travis Kalanick to help fund his acquisition of Pony.ai's US arm; Pony.ai has permits to operate robo taxis in the US and China (Mike Isaac/New York Times)

Uber in Talks With Travis Kalanick to Fund Self-Driving Car Deal
The ride-hailing company is in talks to help Mr. Kalanick, who was forced out in 2017, purchase an autonomous vehicles start-up as robot taxi service Waymo gains momentum.

A Grassroots Network and Community Roadmap for Interconnected Autonomous Science Laboratories for Accelerated Discovery
Rafael Ferreira da Silva, Milad Abolhasani, Dionysios A. Antonopoulos, Laura Biven, Ryan Coffee, Ian T. Foster, Leslie Hamilton, Shantenu Jha, Theresa Mayer, Benjamin Mintz, Robert G. Moore, Salahudin Nimer, Noah Paulson, Woong Shin, Frederic Suter, Mitra Taheri, Michela Taufer, Newell R. Washburn

A Grassroots Network and Community Roadmap for Interconnected Autonomous Science Laboratories for Accelerated Discovery
Scientific discovery is being revolutionized by AI and autonomous systems, yet current autonomous laboratories remain isolated islands unable to collaborate across institutions. We present the Autonomous Interconnected Science Lab Ecosystem (AISLE), a grassroots network transforming fragmented capabilities into a unified system that shorten the path from ideation to innovation to impact and accelerates discovery from decades to months. AISLE addresses five critical dimensions: (1) cross-institu…

Vertex addition to a ball graph with application to reliability and area coverage in autonomous swarms
Calum Buchanan, Puck Rombach, James Bagrow, Hamid R. Ossareh
https://arxiv.org/abs/2506.19197

Vertex addition to a ball graph with application to reliability and area coverage in autonomous swarms
A unit ball graph consists of a set of vertices, labeled by points in Euclidean space, and edges joining all pairs of points within distance $1$. These geometric graphs are used to model a variety of spatial networks, including communication networks between agents in an autonomous swarm. In such an application, vertices and/or edges of the graph may not be perfectly reliable; an agent may experience failure or a communication link rendered inoperable. With the goal of designing robust swarm fo…

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos
Yitong Quan, Benjamin Kiefer, Martin Messmer, Andreas Zell
https://arxiv.org/abs/2506.20550

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos
Modern image-based object detection models, such as YOLOv7, primarily process individual frames independently, thus ignoring valuable temporal context naturally present in videos. Meanwhile, existing video-based detection methods often introduce complex temporal modules, significantly increasing model size and computational complexity. In practical applications such as surveillance and autonomous driving, transient challenges including motion blur, occlusions, and abrupt appearance changes can …

route_views: Route Views AS graphs (1997-1998)
733 daily network snapshots denoting BGP traffic among autonomous systems (ASs) on the Internet, from the Oregon Route Views Project, spanning 8 November 1997 to 2 January 2000. Data collected by NLANR/MOAT.
This network has 3414 nodes and 6574 edges.
Tags: Technological, Communication, Unweighted, Temporal

Crowdsourcing eHMI Designs: A Participatory Approach to Autonomous Vehicle-Pedestrian Communication
Ronald Cumbal, Didem Gurdur Broo, Ginevra Castellano
https://arxiv.org/abs/2506.18605

Crowdsourcing eHMI Designs: A Participatory Approach to Autonomous Vehicle-Pedestrian Communication
As autonomous vehicles become more integrated into shared human environments, effective communication with road users is essential for ensuring safety. While previous research has focused on developing external Human-Machine Interfaces (eHMIs) to facilitate these interactions, we argue that involving users in the early creative stages can help address key challenges in the development of this technology. To explore this, our study adopts a participatory, crowd-sourced approach to gather user-ge…

Regression Testing Optimization for ROS-based Autonomous Systems: A Comprehensive Review of Techniques
Yupeng Jiang, Shuaiyi Sun, Xi Zheng
https://arxiv.org/abs/2506.16101

Regression Testing Optimization for ROS-based Autonomous Systems: A Comprehensive Review of Techniques
Regression testing plays a critical role in maintaining software reliability, particularly for ROS-based autonomous systems (ROSAS), which frequently undergo continuous integration and iterative development. However, conventional regression testing techniques face significant challenges when applied to autonomous systems due to their dynamic and non-deterministic behaviors, complex multi-modal sensor data, asynchronous distributed architectures, and stringent safety and real-time constraints. A…

Leveraging Large Language Model for Intelligent Log Processing and Autonomous Debugging in Cloud AI Platforms
Cheng Ji, Huaiying Luo
https://arxiv.org/abs/2506.17900

Leveraging Large Language Model for Intelligent Log Processing and Autonomous Debugging in Cloud AI Platforms
With the increasing complexity and rapid expansion of the scale of AI systems in cloud platforms, the log data generated during system operation is massive, unstructured, and semantically ambiguous, which brings great challenges to fault location and system self-repair. In order to solve this problem, this paper proposes an intelligent log processing and automatic debugging framework based on Large Language Model (LLM), named Intelligent Debugger (LLM-ID). This method is extended on the basis o…

Global Attractors of Non-autonomous Lattice Dynamical Systems
David Cheban, Andrei Sultan
https://arxiv.org/abs/2506.17841 https://ar…

Global Attractors of Non-autonomous Lattice Dynamical Systems
The aim of this paper is studying the compact global attractors for non-autonomous lattice dynamical systems of the form $u_{i}'=ν(u_{i-1}-2u_i+u_{i+1})-λu_{i}+f(u_i)+f_{i}(t)\ (i\in \mathbb Z,\ λ>0)$. We prove their dissipativness, asymptotic compactness and then the existence of compact global attractors.

Differential Transformer-driven 6G Physical Layer for Collaborative Perception Enhancement
Soheyb Ribouh, Osama Saleem, Mohamed Ababsa
https://arxiv.org/abs/2506.20597

Differential Transformer-driven 6G Physical Layer for Collaborative Perception Enhancement
The emergence of 6G wireless networks promises to revolutionize vehicular communications by enabling ultra-reliable, low-latency, and high-capacity data exchange. In this context, collaborative perception techniques, where multiple vehicles or infrastructure nodes cooperate to jointly receive and decode transmitted signals, aim to enhance reliability and spectral efficiency for Connected Autonomous Vehicle (CAV) applications. In this paper, we propose an end-to-end wireless neural receiver base…

Quantum Artificial Intelligence for Secure Autonomous Vehicle Navigation: An Architectural Proposal
Hemanth Kannamarlapudi, Sowmya Chintalapudi
https://arxiv.org/abs/2506.16000

Quantum Artificial Intelligence for Secure Autonomous Vehicle Navigation: An Architectural Proposal
Navigation is a very crucial aspect of autonomous vehicle ecosystem which heavily relies on collecting and processing large amounts of data in various states and taking a confident and safe decision to define the next vehicle maneuver. In this paper, we propose a novel architecture based on Quantum Artificial Intelligence by enabling quantum and AI at various levels of navigation decision making and communication process in Autonomous vehicles : Quantum Neural Networks for multimodal sensor fus…

Shield AI has emerged as a lead contender in the increasingly crowded race against competitors like Anduril and AeroVironment
to outfit the U.S. military with killer drones.
The $5 billion-valued startup, founded in 2015 by brothers Ryan and Brandon Tseng,
sells a suite of hardware and products, including autonomous piloting software that has been used to fly fighter jets.
In March, it raised $240 million in a funding round led by Korean conglomerate Hanwha and Amer…

How A Grisly Injury Threw $5 Billion Drone Startup Shield AI Off Course
Shield AI executives overlooked its V-BAT drone’s safety hazards. With sales falling short and the CEO stepping down, the company is trying to win back military customers.

Communication Architecture for Autonomous Power-to-X Platforms: Enhancing Inspection and Operation With Legged Robots and 5G
Peter Frank, Falk Dettinger, Daniel Dittler, Pascal H\"abig, Nasser Jazdi, Kai Hufendiek, Michael Weyrich
https://arxiv.org/abs/2506.18572

Communication Architecture for Autonomous Power-to-X Platforms: Enhancing Inspection and Operation With Legged Robots and 5G
Inspection and maintenance of offshore platforms are associated with high costs, primarily due to the significant personnel requirements and challenging operational conditions. This paper first presents a classification of Power to X platforms. Building upon this foundation, a communication architecture is proposed to enable monitoring, control, and teleoperation for a Power to X platform. To reduce the demand for human labor, a robotic system is integrated to autonomously perform inspection an…

Safety-Aware Optimal Scheduling for Autonomous Masonry Construction using Collaborative Heterogeneous Aerial Robots
Marios-Nektarios Stamatopoulos, Shridhar Velhal, Avijit Banerjee, George Nikolakopoulos
https://arxiv.org/abs/2506.18697

Safety-Aware Optimal Scheduling for Autonomous Masonry Construction using Collaborative Heterogeneous Aerial Robots
This paper presents a novel high-level task planning and optimal coordination framework for autonomous masonry construction, using a team of heterogeneous aerial robotic workers, consisting of agents with separate skills for brick placement and mortar application. This introduces new challenges in scheduling and coordination, particularly due to the mortar curing deadline required for structural bonding and ensuring the safety constraints among UAVs operating in parallel. To address this, an au…

Public Perceptions of Autonomous Vehicles: A Survey of Pedestrians and Cyclists in Pittsburgh
Rudra Y. Bedekar
https://arxiv.org/abs/2506.17513 https://

Public Perceptions of Autonomous Vehicles: A Survey of Pedestrians and Cyclists in Pittsburgh
This study investigates how autonomous vehicle(AV) technology is perceived by pedestrians and bicyclists in Pittsburgh. Using survey data from over 1200 respondents, the research explores the interplay between demographics, AV interactions, infrastructural readiness, safety perceptions, and trust. Findings highlight demographic divides, infrastructure gaps, and the crucial role of communication and education in AV adoption.

Complex Model Transformations by Reinforcement Learning with Uncertain Human Guidance
Kyanna Dagenais, Istvan David
https://arxiv.org/abs/2506.20883 https:…

Complex Model Transformations by Reinforcement Learning with Uncertain Human Guidance
Model-driven engineering problems often require complex model transformations (MTs), i.e., MTs that are chained in extensive sequences. Pertinent examples of such problems include model synchronization, automated model repair, and design space exploration. Manually developing complex MTs is an error-prone and often infeasible process. Reinforcement learning (RL) is an apt way to alleviate these issues. In RL, an autonomous agent explores the state space through trial and error to identify benef…

Waymo applied for a NYC permit to test its cars with safety drivers and plans to start collecting mapping data with manually driven cars in Manhattan in July (Andrew J. Hawkins/The Verge)
https://www.theverge.com/news/689093/waymo-nyc-permit…

Waymo’s robotaxis are coming back to New York City
Waymo applied for a permit to test autonomous vehicles with safety drivers in New York City. And it hopes it can change the state law to allow it to operate fully driverless.

A Study on E2E Performance Improvement of Platooning Using Outdoor LiFi
Zhiyi Zhu, Eiji Takimoto, Patrick Finnerty, Chikara Ohta
https://arxiv.org/abs/2506.19304

A Study on E2E Performance Improvement of Platooning Using Outdoor LiFi
Platooning within autonomous vehicles has proven effective in addressing driver shortages and reducing fuel consumption. However, as platooning lengths increase, traditional C-V2X (cellular vehicle-to-everything) architectures are susceptible to end-to-end (E2E) latency increases. This is due to the necessity of relaying information through multiple hops from the leader vehicle to the last vehicle. To address this problem, this paper proposes a hybrid communication architecture based on a simul…

Finding the Easy Way Through -- the Probabilistic Gap Planner for Social Robot Navigation
Malte Probst, Raphael Wenzel, Tim Puphal, Monica Dasi, Nico A. Steinhardt, Sango Matsuzaki, Misa Komuro
https://arxiv.org/abs/2506.20320

Finding the Easy Way Through -- the Probabilistic Gap Planner for Social Robot Navigation
In Social Robot Navigation, autonomous agents need to resolve many sequential interactions with other agents. State-of-the art planners can efficiently resolve the next, imminent interaction cooperatively and do not focus on longer planning horizons. This makes it hard to maneuver scenarios where the agent needs to select a good strategy to find gaps or channels in the crowd. We propose to decompose trajectory planning into two separate steps: Conflict avoidance for finding good, macroscopic tr…

Leveraging Cloud-Fog Automation for Autonomous Collision Detection and Classification in Intelligent Unmanned Surface Vehicles
Thien Tran, Quang Nguyen, Jonathan Kua, Minh Tran, Toan Luu, Thuong Hoang, Jiong Jin
https://arxiv.org/abs/2506.18024

Leveraging Cloud-Fog Automation for Autonomous Collision Detection and Classification in Intelligent Unmanned Surface Vehicles
Industrial Cyber-Physical Systems (ICPS) technologies are foundational in driving maritime autonomy, particularly for Unmanned Surface Vehicles (USVs). However, onboard computational constraints and communication latency significantly restrict real-time data processing, analysis, and predictive modeling, hence limiting the scalability and responsiveness of maritime ICPS. To overcome these challenges, we propose a distributed Cloud-Edge-IoT architecture tailored for maritime ICPS by leveraging d…

AI is the Strategy: From Agentic AI to Autonomous Business Models onto Strategy in the Age of AI
Ren\'e Bohnsack, Mickie de Wet
https://arxiv.org/abs/2506.17339

AI is the Strategy: From Agentic AI to Autonomous Business Models onto Strategy in the Age of AI
This article develops the concept of Autonomous Business Models (ABMs) as a distinct managerial and strategic logic in the age of agentic AI. While most firms still operate within human-driven or AI-augmented models, we argue that we are now entering a phase where agentic AI (systems capable of initiating, coordinating, and adapting actions autonomously) can increasingly execute the core mechanisms of value creation, delivery, and capture. This shift reframes AI not as a tool to support strateg…

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects
Shulan Ruan, Rongwei Wang, Xuchen Shen, Huijie Liu, Baihui Xiao, Jun Shi, Kun Zhang, Zhenya Huang, Yu Liu, Enhong Chen, You He
https://arxiv.org/abs/2506.19769

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects
Multi-sensor fusion perception (MSFP) is a key technology for embodied AI, which can serve a variety of downstream tasks (e.g., 3D object detection and semantic segmentation) and application scenarios (e.g., autonomous driving and swarm robotics). Recently, impressive achievements on AI-based MSFP methods have been reviewed in relevant surveys. However, we observe that the existing surveys have some limitations after a rigorous and detailed investigation. For one thing, most surveys are oriente…

Algorithmic Approaches to Enhance Safety in Autonomous Vehicles: Minimizing Lane Changes and Merging
Seyed Moein Abtahi, Akramul Azim
https://arxiv.org/abs/2506.15026

Algorithmic Approaches to Enhance Safety in Autonomous Vehicles: Minimizing Lane Changes and Merging
The rapid advancements in autonomous vehicle (AV) technology promise enhanced safety and operational efficiency. However, frequent lane changes and merging maneuvers continue to pose significant safety risks and disrupt traffic flow. This paper introduces the Minimizing Lane Change Algorithm (MLCA), a state-machine-based approach designed to reduce unnecessary lane changes, thereby enhancing both traffic safety and efficiency. The MLCA algorithm prioritizes maintaining lane stability unless saf…

Spatial Optimization of Autonomous Vehicle Assignment Based on Distance-Driven Demand and Customer Patience
Niloufar Mirzavand Boroujeni, Nasim Mirzavand Boroujeni, Nima Moradi, Saeed Jamalzadeh
https://arxiv.org/abs/2506.12671

Spatial Optimization of Autonomous Vehicle Assignment Based on Distance-Driven Demand and Customer Patience
Autonomous vehicles (AVs) can improve efficiency, reduce costs, and enhance road safety. They optimize traffic flow, minimize congestion, and support sustainability through shared mobility and reduced fuel consumption. A key challenge in AV deployment is allocating vehicles to parking lots across regions to meet fluctuating demand. Proper allocation reduces delays, lowers costs, and boosts user satisfaction by ensuring timely vehicle availability. This paper explores the impact of customer wait…

Agentic Markets: Game Dynamics and Equilibrium in Markets with Learning Agents
Martin Bichler, Julius Durmann, Matthias Oberlechner
https://arxiv.org/abs/2506.18571

Agentic Markets: Game Dynamics and Equilibrium in Markets with Learning Agents
Autonomous and learning agents increasingly participate in markets - setting prices, placing bids, ordering inventory. Such agents are not just aiming to optimize in an uncertain environment; they are making decisions in a game-theoretical environment where the decision of one agent influences the profit of other agents. While game theory usually predicts outcomes of strategic interaction as an equilibrium, it does not capture how repeated interaction of learning agents arrives at a certain out…

ADRD: LLM-Driven Autonomous Driving Based on Rule-based Decision Systems
Fanzhi Zeng, Siqi Wang, Chuzhao Zhu, Li Li
https://arxiv.org/abs/2506.14299 https:…

ADRD: LLM-Driven Autonomous Driving Based on Rule-based Decision Systems
How to construct an interpretable autonomous driving decision-making system has become a focal point in academic research. In this study, we propose a novel approach that leverages large language models (LLMs) to generate executable, rule-based decision systems to address this challenge. Specifically, harnessing the strong reasoning and programming capabilities of LLMs, we introduce the ADRD(LLM-Driven Autonomous Driving Based on Rule-based Decision Systems) framework, which integrates three co…

PSALM-V: Automating Symbolic Planning in Interactive Visual Environments with Large Language Models
Wang Bill Zhu, Miaosen Chai, Ishika Singh, Robin Jia, Jesse Thomason
https://arxiv.org/abs/2506.20097

PSALM-V: Automating Symbolic Planning in Interactive Visual Environments with Large Language Models
We propose PSALM-V, the first autonomous neuro-symbolic learning system able to induce symbolic action semantics (i.e., pre- and post-conditions) in visual environments through interaction. PSALM-V bootstraps reliable symbolic planning without expert action definitions, using LLMs to generate heuristic plans and candidate symbolic semantics. Previous work has explored using large language models to generate action semantics for Planning Domain Definition Language (PDDL)-based symbolic planners.…

Varanus: Runtime Verification for CSP
Matt Luckcuck, Angelo Ferrando, Fatma Faruq
https://arxiv.org/abs/2506.14426 https://arxiv.org/…

Varanus: Runtime Verification for CSP
Autonomous systems are often used in changeable and unknown environments, where traditional verification may not be suitable. Runtime Verification (RV) checks events performed by a system against a formal specification of its intended behaviour, making it highly suitable for ensuring that an autonomous system is obeying its specification at runtime. Communicating Sequential Processes (CSP) is a process algebra usually used in static verification, which captures behaviour as a trace of events, m…

Eventual, which develops Daft, a Python-native open-source data processing engine, raised a $7.5M seed led by CRV and a $20M Series A led by Felicis (Rebecca Szkutak/TechCrunch)
https://techcrunch.com/2025/06/24/how-a-data-pro…

How a data processing problem at Lyft became the basis for Eventual | TechCrunch
Eventual's data processing engine Daft was inspried by the founders' experience working on Lyft's autonomous vehicle project.

AutoSAS: a new human-aside-the-loop paradigm for automated SAS fitting for high throughput and autonomous experimentation
Duncan R. Sutherland, Rachel Ford, Yun Liu, Tyler B. Martin, Peter A. Beaucage
https://arxiv.org/abs/2506.13918

AutoSAS: a new human-aside-the-loop paradigm for automated SAS fitting for high throughput and autonomous experimentation
The advancement of artificial-intelligence driven autonomous experiments demands physics-based modeling and decision-making processes, not only to improve the accuracy of the experimental trajectory but also to increase trust by allowing transparent human-machine collaboration. High-quality structural characterization techniques (e.g., X-ray, neutron, or static light scattering) are a particularly relevant example of this need: they provide invaluable information but are challenging to analyze …

A Computationally Aware Multi Objective Framework for Camera LiDAR Calibration
Venkat Karramreddy, Rangarajan Ramanujam
https://arxiv.org/abs/2506.20636 ht…

A Computationally Aware Multi Objective Framework for Camera LiDAR Calibration
Accurate extrinsic calibration between LiDAR and camera sensors is important for reliable perception in autonomous systems. In this paper, we present a novel multi-objective optimization framework that jointly minimizes the geometric alignment error and computational cost associated with camera-LiDAR calibration. We optimize two objectives: (1) error between projected LiDAR points and ground-truth image edges, and (2) a composite metric for computational cost reflecting runtime and resource usa…

Floquet-like theory and gauge transformations for general smooth dynamical systems
Giuseppe Gaeta, Sebastian Walcher
https://arxiv.org/abs/2506.14982 https…

Floquet-like theory and gauge transformations for general smooth dynamical systems
The classical Floquet theory allows to map a time-periodic system of linear differential equations into an autonomous one. By looking at it in a geometrical way, we extend the theory to a class of non-autonomous non-periodic equations. This is obtained by considering a change of variables which depends on time in a non-trivial way, i.e. introducing gauge transformations, well known in fundamental Physics and Field Theory -- but which seems to have received little attention in this context.

Towards Zero-Shot Coordination between Teams of Agents: The N-XPlay Framework
Ava Abderezaei, Chi-Hui Lin, Joseph Miceli, Naren Sivagnanadasan, St\'ephane Aroca-Ouellette, Jake Brawer, Alessandro Roncone
https://arxiv.org/abs/2506.17560

Towards Zero-Shot Coordination between Teams of Agents: The N-XPlay Framework
Zero-shot coordination (ZSC) -- the ability to collaborate with unfamiliar partners -- is essential to making autonomous agents effective teammates. Existing ZSC methods evaluate coordination capabilities between two agents who have not previously interacted. However, these scenarios do not reflect the complexity of real-world multi-agent systems, where coordination often involves a hierarchy of sub-groups and interactions between teams of agents, known as Multi-Team Systems (MTS). To address t…

STM32-Based IoT Framework for Real-Time Environmental Monitoring and Wireless Node Synchronization
Ahmed Faizul Haque Dhrubo, Sazid Hasan, Mohammad Abdul Qayum
https://arxiv.org/abs/2506.17295

STM32-Based IoT Framework for Real-Time Environmental Monitoring and Wireless Node Synchronization
The fast pace of technological growth has created a heightened need for intelligent, autonomous monitoring systems in a variety of fields, especially in environmental applications. This project shows the design process and implementation of a proper dual node (master-slave) IoT-based monitoring system using STM32F103C8T6 microcontrollers. The structure of the wireless monitoring system studies the environmental conditions in real-time and can measure parameters like temperature, humidity, soil …

3D Gaussian Splatting for Fine-Detailed Surface Reconstruction in Large-Scale Scene
Shihan Chen, Zhaojin Li, Zeyu Chen, Qingsong Yan, Gaoyang Shen, Ran Duan
https://arxiv.org/abs/2506.17636

3D Gaussian Splatting for Fine-Detailed Surface Reconstruction in Large-Scale Scene
Recent developments in 3D Gaussian Splatting have made significant advances in surface reconstruction. However, scaling these methods to large-scale scenes remains challenging due to high computational demands and the complex dynamic appearances typical of outdoor environments. These challenges hinder the application in aerial surveying and autonomous driving. This paper proposes a novel solution to reconstruct large-scale surfaces with fine details, supervised by full-sized images. Firstly, we…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/6]:
- 4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving
Wenzel, Wang, Yang, Cheng, Khan, von Stumberg, Zeller, Cremers

Robust Embodied Self-Identification of Morphology in Damaged Multi-Legged Robots
Sahand Farghdani, Mili Patel, Robin Chhabra
https://arxiv.org/abs/2506.19984

Robust Embodied Self-Identification of Morphology in Damaged Multi-Legged Robots
Multi-legged robots (MLRs) are vulnerable to leg damage during complex missions, which can impair their performance. This paper presents a self-modeling and damage identification algorithm that enables autonomous adaptation to partial or complete leg loss using only data from a low-cost IMU. A novel FFT-based filter is introduced to address time-inconsistent signals, improving damage detection by comparing body orientation between the robot and its model. The proposed method identifies damaged …

caida_as: CAIDA AS graphs (2004-2007)
A sequence of 122 network snapshots denoting Autonomous System (AS) relationships on the Internet, from 2004-2007, inferred using the Serial-1 method from RouteViews BGP table snapshots and a set of heuristics.
This network has 23072 nodes and 78684 edges.
Tags: Technological, Communication, Unweighted, Temporal

Mixed Traffic: A Perspective from Long Duration Autonomy
Filippos Tzortzoglou, Logan E. Beaver
https://arxiv.org/abs/2506.15004 https://

Mixed Traffic: A Perspective from Long Duration Autonomy
The rapid adoption of autonomous vehicle has established mixed traffic environments, comprising both autonomous and human-driven vehicles (HDVs), as essential components of next-generation mobility systems. Along these lines, connectivity between autonomous vehicles and infrastructure (V2I) is also a significant factor that can effectively support higher-level decision-making. At the same time, the integration of V2I within mixed traffic environments remains a timely and challenging problem. In…

Toward Safety-First Human-Like Decision Making for Autonomous Vehicles in Time-Varying Traffic Flow
Xiao Wang, Junru Yu, Jun Huang, Qiong Wu, Ljubo Vacic, Changyin Sun
https://arxiv.org/abs/2506.14502

Toward Safety-First Human-Like Decision Making for Autonomous Vehicles in Time-Varying Traffic Flow
Despite the recent advancements in artificial intelligence technologies have shown great potential in improving transport efficiency and safety, autonomous vehicles(AVs) still face great challenge of driving in time-varying traffic flow, especially in dense and interactive situations. Meanwhile, human have free wills and usually do not make the same decisions even situate in the exactly same scenarios, leading to the data-driven methods suffer from poor migratability and high search cost proble…

Real-Time Obstacle Avoidance Algorithms for Unmanned Aerial and Ground Vehicles
Jingwen Wei
https://arxiv.org/abs/2506.20311 https://…

Real-Time Obstacle Avoidance Algorithms for Unmanned Aerial and Ground Vehicles
The growing use of mobile robots in sectors such as automotive, agriculture, and rescue operations reflects progress in robotics and autonomy. In unmanned aerial vehicles (UAVs), most research emphasizes visual SLAM, sensor fusion, and path planning. However, applying UAVs to search and rescue missions in disaster zones remains underexplored, especially for autonomous navigation. This report develops methods for real-time and secure UAV maneuvering in complex 3D environments, crucial during f…

Coherent Track-Before-Detect
Mingchao Liang, Florian Meyer
https://arxiv.org/abs/2506.18177 https://arxiv.org/pdf/2506.18177

Coherent Track-Before-Detect
Accurately tracking an unknown and time-varying number of objects in complex environments is a significant challenge but a fundamental capability in a variety of applications, including applied ocean sciences, surveillance, autonomous driving, and wireless communications. Conventional Bayesian multiobject tracking (MOT) methods typically employ a detect-then-track (DTT) approach, where a frontend detector preprocesses raw sensor data to extract measurements for MOT. The irreversible nature of t…

A Step-by-Step Guide to Creating a Robust Autonomous Drone Testing Pipeline
Yupeng Jiang, Yao Deng, Sebastian Schroder, Linfeng Liang, Suhaas Gambhir, Alice James, Avishkar Seth, James Pirrie, Yihao Zhang, Xi Zheng
https://arxiv.org/abs/2506.11400

A Step-by-Step Guide to Creating a Robust Autonomous Drone Testing Pipeline
Autonomous drones are rapidly reshaping industries ranging from aerial delivery and infrastructure inspection to environmental monitoring and disaster response. Ensuring the safety, reliability, and efficiency of these systems is paramount as they transition from research prototypes to mission-critical platforms. This paper presents a step-by-step guide to establishing a robust autonomous drone testing pipeline, covering each critical stage: Software-in-the-Loop (SIL) Simulation Testing, Hardwa…

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries
Zhiqiang Lin, Huan Sun, Ness Shroff
https://arxiv.org/abs/2506.18932 https://…

AI Safety vs. AI Security: Demystifying the Distinction and Boundaries
Artificial Intelligence (AI) is rapidly being integrated into critical systems across various domains, from healthcare to autonomous vehicles. While its integration brings immense benefits, it also introduces significant risks, including those arising from AI misuse. Within the discourse on managing these risks, the terms "AI Safety" and "AI Security" are often used, sometimes interchangeably, resulting in conceptual confusion. This paper aims to demystify the distinction and delineate the prec…

CFTel: A Practical Architecture for Robust and Scalable Telerobotics with Cloud-Fog Automation
Thien Tran, Jonathan Kua, Minh Tran, Honghao Lyu, Thuong Hoang, Jiong Jin
https://arxiv.org/abs/2506.17991

CFTel: A Practical Architecture for Robust and Scalable Telerobotics with Cloud-Fog Automation
Telerobotics is a key foundation in autonomous Industrial Cyber-Physical Systems (ICPS), enabling remote operations across various domains. However, conventional cloud-based telerobotics suffers from latency, reliability, scalability, and resilience issues, hindering real-time performance in critical applications. Cloud-Fog Telerobotics (CFTel) builds on the Cloud-Fog Automation (CFA) paradigm to address these limitations by leveraging a distributed Cloud-Edge-Robotics computing architecture, e…

Deep Research Agents: A Systematic Examination And Roadmap
Yuxuan Huang, Yihang Chen, Haozheng Zhang, Kang Li, Meng Fang, Linyi Yang, Xiaoguang Li, Lifeng Shang, Songcen Xu, Jianye Hao, Kun Shao, Jun Wang
https://arxiv.org/abs/2506.18096

Deep Research Agents: A Systematic Examination And Roadmap
The rapid progress of Large Language Models (LLMs) has given rise to a new category of autonomous AI systems, referred to as Deep Research (DR) agents. These agents are designed to tackle complex, multi-turn informational research tasks by leveraging a combination of dynamic reasoning, adaptive long-horizon planning, multi-hop information retrieval, iterative tool use, and the generation of structured analytical reports. In this paper, we conduct a detailed analysis of the foundational technolo…

Experimental Setup and Software Pipeline to Evaluate Optimization based Autonomous Multi-Robot Search Algorithms
Aditya Bhatt, Mary Katherine Corra, Franklin Merlo, Prajit KrisshnaKumar, Souma Chowdhury
https://arxiv.org/abs/2506.16710

Experimental Setup and Software Pipeline to Evaluate Optimization based Autonomous Multi-Robot Search Algorithms
Signal source localization has been a problem of interest in the multi-robot systems domain given its applications in search \& rescue and hazard localization in various industrial and outdoor settings. A variety of multi-robot search algorithms exist that usually formulate and solve the associated autonomous motion planning problem as a heuristic model-free or belief model-based optimization process. Most of these algorithms however remains tested only in simulation, thereby losing the opportu…

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol
Haowen Xu, Yulin Sun, Jose Tupayachi, Olufemi Omitaomu, Sisi Zlatanov, Xueping Li
https://arxiv.org/abs/2506.13068

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol
Optimizing urban freight logistics is critical for developing sustainable, low-carbon cities. Traditional methods often rely on manual coordination of simulation tools, optimization solvers, and expert-driven workflows, limiting their efficiency and scalability. This paper presents an agentic system architecture that leverages the model context protocol (MCP) to orchestrate multi-agent collaboration among scientific tools for autonomous, simulation-informed optimization in urban logistics. The …

Bedrock Ocean, which has developed an autonomous underwater vehicle to map the ocean floor using sonar and magnetic sensors, raised a $25M Series A-2 (Tim De Chant/TechCrunch)
https://techcrunch.com/2025/06/10/bedrock-ocean-dredges-u…

Exclusive: Bedrock Ocean dredges up $25M to map the seafloor with robots | TechCrunch
Bedrock Ocean wants to use autonomous underwater vehicles to make mapping the seafloor faster and cheaper.

Conformal Safety Shielding for Imperfect-Perception Agents
William Scarbro, Calum Imrie, Sinem Getir Yaman, Kavan Fatehi, Corina S. Pasareanu, Radu Calinescu, Ravi Mangal
https://arxiv.org/abs/2506.17275

Conformal Safety Shielding for Imperfect-Perception Agents
We consider the problem of safe control in discrete autonomous agents that use learned components for imperfect perception (or more generally, state estimation) from high-dimensional observations. We propose a shield construction that provides run-time safety guarantees under perception errors by restricting the actions available to an agent, modeled as a Markov decision process, as a function of the state estimates. Our construction uses conformal prediction for the perception component, which…

route_views: Route Views AS graphs (1997-1998)
733 daily network snapshots denoting BGP traffic among autonomous systems (ASs) on the Internet, from the Oregon Route Views Project, spanning 8 November 1997 to 2 January 2000. Data collected by NLANR/MOAT.
This network has 5016 nodes and 10542 edges.
Tags: Technological, Communication, Unweighted, Temporal

Zero-Shot Scene Understanding with Multimodal Large Language Models for Automated Vehicles
Mohammed Elhenawy, Shadi Jaradat, Taqwa I. Alhadidi, Huthaifa I. Ashqar, Ahmed Jaber, Andry Rakotonirainy, Mohammad Abu Tami
https://arxiv.org/abs/2506.12232

Zero-Shot Scene Understanding with Multimodal Large Language Models for Automated Vehicles
Scene understanding is critical for various downstream tasks in autonomous driving, including facilitating driver-agent communication and enhancing human-centered explainability of autonomous vehicle (AV) decisions. This paper evaluates the capability of four multimodal large language models (MLLMs), including relatively small models, to understand scenes in a zero-shot, in-context learning setting. Additionally, we explore whether combining these models using an ensemble approach with majority…

May the Feedback Be with You! Unlocking the Power of Feedback-Driven Deep Learning Framework Fuzzing via LLMs
Shaoyu Yang, Chunrong Fang, Haifeng Lin, Xiang Chen, Zhenyu Chen
https://arxiv.org/abs/2506.17642

May the Feedback Be with You! Unlocking the Power of Feedback-Driven Deep Learning Framework Fuzzing via LLMs
Artificial Intelligence (AI) Infrastructures, represented by Deep Learning (DL) frameworks, have served as fundamental DL systems over the last decade. However, the bugs in DL frameworks could lead to catastrophic consequences in some critical scenarios (e.g., healthcare and autonomous driving). A simple yet effective way to find bugs in DL frameworks is fuzz testing (Fuzzing). Unfortunately, existing fuzzing techniques have not comprehensively considered multiple types of feedback. Additionall…

Q&A with Yoshua Bengio on his nonprofit LawZero focusing on AI safety, its research direction, risks of increasingly capable and autonomous AI systems, and more (Sigal Samuel/Vox)
https://www.vox.com/future-perfect/417087/ai-safety-yoshua-bengio-lawze…

He’s the godfather of AI. Now, he has a bold new plan to keep us safe from it.
Humans — not AI systems — should be the ones with agency, says the godfather of AI.

Dynamic Knowledge Exchange and Dual-diversity Review: Concisely Unleashing the Potential of a Multi-Agent Research Team
Weilun Yu, Shixiang Tang, Yonggui Huang, Nanqing Dong, Li Fan, Honggang Qi, Wei Liu, Xiaoli Diao, Xi Chen, Wanli Ouyang
https://arxiv.org/abs/2506.18348

Dynamic Knowledge Exchange and Dual-diversity Review: Concisely Unleashing the Potential of a Multi-Agent Research Team
Scientific progress increasingly relies on effective collaboration among researchers, a dynamic that large language models (LLMs) have only begun to emulate. While recent LLM-based scientist agents show promise in autonomous scientific discovery, they often lack the interactive reasoning and evaluation mechanisms essential to real-world research. We propose IDVSCI (Internal Discussion and Vote SCIentists), a multi-agent framework built on LLMs that incorporates two key innovations: a Dynamic Kn…

Second Order State Hallucinations for Adversarial Attack Mitigation in Formation Control of Multi-Agent Systems
Laksh Patel, Akhilesh Raj
https://arxiv.org/abs/2506.17283

Second Order State Hallucinations for Adversarial Attack Mitigation in Formation Control of Multi-Agent Systems
The increasing deployment of multi-agent systems (MAS) in critical infrastructures such as autonomous transportation, disaster relief, and smart cities demands robust formation control mechanisms resilient to adversarial attacks. Traditional consensus-based controllers, while effective under nominal conditions, are highly vulnerable to data manipulation, sensor spoofing, and communication failures. To address this challenge, we propose Second-Order State Hallucination (SOSH), a novel framework …

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy
Weitao Zhou, Bo Zhang, Zhong Cao, Xiang Li, Qian Cheng, Chunyang Liu, Yaqin Zhang, Diange Yang
https://arxiv.org/abs/2506.16720

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy
With the increasing presence of automated vehicles on open roads under driver supervision, disengagement cases are becoming more prevalent. While some data-driven planning systems attempt to directly utilize these disengagement cases for policy improvement, the inherent scarcity of disengagement data (often occurring as a single instances) restricts training effectiveness. Furthermore, some disengagement data should be excluded since the disengagement may not always come from the failure of dri…

AI-based Multimodal Biometrics for Detecting Smartphone Distractions: Application to Online Learning
Alvaro Becerra, Roberto Daza, Ruth Cobos, Aythami Morales, Mutlu Cukurova, Julian Fierrez
https://arxiv.org/abs/2506.17364

AI-based Multimodal Biometrics for Detecting Smartphone Distractions: Application to Online Learning
This work investigates the use of multimodal biometrics to detect distractions caused by smartphone use during tasks that require sustained attention, with a focus on computer-based online learning. Although the methods are applicable to various domains, such as autonomous driving, we concentrate on the challenges learners face in maintaining engagement amid internal (e.g., motivation), system-related (e.g., course design) and contextual (e.g., smartphone use) factors. Traditional learning plat…

Tesla's robotaxi service will launch in Austin with just ~10 cars that are geofenced to avoid complex intersections and monitored by backup teleoperators (Richard Waters/Financial Times)
https://www.ft.com/content/2ad302c6-febe-448f-9980-17d116d9695a…

Tesla’s robotaxi ambitions face a reality check after launch
Autonomous ride-hailing service to make tentative debut in Austin, Texas, with only about 10 cars

Revolutionizing Validation and Verification: Explainable Testing Methodologies for Intelligent Automotive Decision-Making Systems
Halit Eris, Stefan Wagner
https://arxiv.org/abs/2506.16876

Revolutionizing Validation and Verification: Explainable Testing Methodologies for Intelligent Automotive Decision-Making Systems
Autonomous Driving Systems (ADS) use complex decision-making (DM) models with multimodal sensory inputs, making rigorous validation and verification (V&V) essential for safety and reliability. These models pose challenges in diagnosing failures, tracing anomalies, and maintaining transparency, with current manual testing methods being inefficient and labor-intensive. This vision paper presents a methodology that integrates explainability, transparency, and interpretability into V&V processes. W…

STSBench: A Spatio-temporal Scenario Benchmark for Multi-modal Large Language Models in Autonomous Driving
Christian Fruhwirth-Reisinger, Du\v{s}an Mali\'c, Wei Lin, David Schinagl, Samuel Schulter, Horst Possegger
https://arxiv.org/abs/2506.06218

STSBench: A Spatio-temporal Scenario Benchmark for Multi-modal Large Language Models in Autonomous Driving
We introduce STSBench, a scenario-based framework to benchmark the holistic understanding of vision-language models (VLMs) for autonomous driving. The framework automatically mines pre-defined traffic scenarios from any dataset using ground-truth annotations, provides an intuitive user interface for efficient human verification, and generates multiple-choice questions for model evaluation. Applied to the NuScenes dataset, we present STSnu, the first benchmark that evaluates the spatio-temporal …

Replaced article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[2/2]:
- Agile, Autonomous Spacecraft Constellations with Disruption Tolerant Networking to Monitor Precip...
Sreeja Roy-Singh, Alan P. Li, Vinay Ravindra, Roderick Lammers, Marc Sanchez Net

RL-Guided MPC for Autonomous Greenhouse Control
Salim Msaad, Murray Harraway, Robert D. McAllister
https://arxiv.org/abs/2506.13278 https://

RL-Guided MPC for Autonomous Greenhouse Control
The efficient operation of greenhouses is essential for enhancing crop yield while minimizing energy costs. This paper investigates a control strategy that integrates Reinforcement Learning (RL) and Model Predictive Control (MPC) to optimize economic benefits in autonomous greenhouses. Previous research has explored the use of RL and MPC for greenhouse control individually, or by using MPC as the function approximator for the RL agent. This study introduces the RL-Guided MPC framework, where a …

Replaced article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[2/2]:
- Experimental Setup and Software Pipeline to Evaluate Optimization based Autonomous Multi-Robot Se...
Aditya Bhatt, Mary Katherine Corra, Franklin Merlo, Prajit KrisshnaKumar, Souma Chowdhury

GenControl: Generative AI-Driven Autonomous Design of Control Algorithms
Chenggang Cui, Jiaming Liu, Peifeng Hui, Pengfeng Lin, Chuanlin Zhang, Frede Blaabjerg
https://arxiv.org/abs/2506.12554

GenControl: Generative AI-Driven Autonomous Design of Control Algorithms
Designing controllers for complex industrial electronic systems is challenging due to nonlinearities and parameter uncertainties, and traditional methods are often slow and costly. To address this, we propose a novel autonomous design framework driven by Large Language Models (LLMs). Our approach employs a bi-level optimization strategy: an LLM intelligently explores and iteratively improves the control algorithm's structure, while a Particle Swarm Optimization (PSO) algorithm efficiently refin…

Behavior Driven Development for 3D Games
Fernando Pastor Ric\'os, Beatriz Mar\'in, I. S. W. B. Prasetya, Tanja E. J. Vos, Joseph Davidson, Karel Hovorka
https://arxiv.org/abs/2506.17057

Behavior Driven Development for 3D Games
Computer 3D games are complex software environments that require novel testing processes to ensure high-quality standards. The Intelligent Verification/Validation for Extended Reality Based Systems (iv4XR) framework addresses this need by enabling the implementation of autonomous agents to automate game testing scenarios. This framework facilitates the automation of regression test cases for complex 3D games like Space Engineers. Nevertheless, the technical expertise required to define test scr…

The UK plans to launch an autonomous taxi trial in London in spring 2026, a year earlier than planned; Uber and Wayve will partner to operate the service (Financial Times)
https://www.ft.com/content/4c9f4025-ec0b-4cdb-9ba5-1c3e77041487

UK to launch trials of driverless taxi services next spring
Britain tries to catch up in the race to adopt autonomous vehicles

Boston-based Maven, which builds autonomous AI agents for enterprises' customer support, raised a $50M Series B led by Dell, taking its total funding to $78M (Chris Metinko/Axios)
https://www.axios.com/pro/enterprise-softw…

Exclusive: Maven AGI raises $50M Series B
The Series B was led by Dell Technologies Capital.

Seeing is Fixing: Cross-Modal Reasoning with Multimodal LLMs for Visual Software Issue Fixing
Kai Huang, Jian Zhang, Xiaofei Xie, Chunyang Chen
https://arxiv.org/abs/2506.16136

Seeing is Fixing: Cross-Modal Reasoning with Multimodal LLMs for Visual Software Issue Fixing
Large language model-(LLM) based automated program repair (APR) techniques have shown promising results in resolving real-world GitHub issue tasks. Existing APR systems are primarily evaluated in unimodal settings (e.g., SWE-bench). However, these autonomous systems struggle to resolve multimodal problem scenarios (e.g., SWE-bench M) due to limitations in interpreting and leveraging visual information. In multimodal scenarios, LLMs need to rely on visual information in the graphical user interf…

Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis
Yuan Gao, Mattia Piccinini, Yuchen Zhang, Dingrui Wang, Korbinian Moller, Roberto Brusnicki, Baha Zarrouki, Alessio Gambi, Jan Frederik Totz, Kai Storms, Steven Peters, Andrea Stocco, Bassam Alrifaee, Marco Pavone, Johannes Betz
https://

Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis
For autonomous vehicles, safe navigation in complex environments depends on handling a broad range of diverse and rare driving scenarios. Simulation- and scenario-based testing have emerged as key approaches to development and validation of autonomous driving systems. Traditional scenario generation relies on rule-based systems, knowledge-driven models, and data-driven synthesis, often producing limited diversity and unrealistic safety-critical cases. With the emergence of foundation models, wh…

Protesters against immigration raids in LA damaged and set several Waymo autonomous taxis on fire on Sunday; Waymo says it is working with law enforcement (Los Angeles Times)
https://www.latimes.com/california/story/2025-06-08…

Waymo vehicles set on fire in downtown L.A, as protesters, police clash
As Los Angeles police struggled with another day of unrest in downtown L.A., several Waymo autonomous taxis were set on fire, sending black smoke billowing into the air.

NetRoller: Interfacing General and Specialized Models for End-to-End Autonomous Driving
Ren Xin, Hongji Liu, Xiaodong Mei, Wenru Liu, Maosheng Ye, Zhili Chen, Jun Ma
https://arxiv.org/abs/2506.14589

NetRoller: Interfacing General and Specialized Models for End-to-End Autonomous Driving
Integrating General Models (GMs) such as Large Language Models (LLMs), with Specialized Models (SMs) in autonomous driving tasks presents a promising approach to mitigating challenges in data diversity and model capacity of existing specialized driving models. However, this integration leads to problems of asynchronous systems, which arise from the distinct characteristics inherent in GMs and SMs. To tackle this challenge, we propose NetRoller, an adapter that incorporates a set of novel mechan…

A Hierarchical Test Platform for Vision Language Model (VLM)-Integrated Real-World Autonomous Driving
Yupeng Zhou, Can Cui, Juntong Peng, Zichong Yang, Juanwu Lu, Jitesh H Panchal, Bin Yao, Ziran Wang
https://arxiv.org/abs/2506.14100

A Hierarchical Test Platform for Vision Language Model (VLM)-Integrated Real-World Autonomous Driving
Vision-Language Models (VLMs) have demonstrated notable promise in autonomous driving by offering the potential for multimodal reasoning through pretraining on extensive image-text pairs. However, adapting these models from broad web-scale data to the safety-critical context of driving presents a significant challenge, commonly referred to as domain shift. Existing simulation-based and dataset-driven evaluation methods, although valuable, often fail to capture the full complexity of real-world …

Causality-aware Safety Testing for Autonomous Driving Systems
Wenbing Tang, Mingfei Cheng, Renzhi Wang, Yuan Zhou, Chengwei Liu, Yang Liu, Zuohua Ding
https://arxiv.org/abs/2506.08688

Causality-aware Safety Testing for Autonomous Driving Systems
Simulation-based testing is essential for evaluating the safety of Autonomous Driving Systems (ADSs). Comprehensive evaluation requires testing across diverse scenarios that can trigger various types of violations under different conditions. While existing methods typically focus on individual diversity metrics, such as input scenarios, ADS-generated motion commands, and system violations, they often fail to capture the complex interrelationships among these elements. This oversight leads to ga…

Improvement on LiDAR-Camera Calibration Using Square Targets
Zhongyuan Li, Honggang Gou, Ping Li, Jiaotong Guo, Mao Ye
https://arxiv.org/abs/2506.18294 htt…

Improvement on LiDAR-Camera Calibration Using Square Targets
Precise sensor calibration is critical for autonomous vehicles as a prerequisite for perception algorithms to function properly. Rotation error of one degree can translate to position error of meters in target object detection at large distance, leading to improper reaction of the system or even safety related issues. Many methods for multi-sensor calibration have been proposed. However, there are very few work that comprehensively consider the challenges of the calibration procedure when appli…

On the Practices of Autonomous Systems Development: Survey-based Empirical Findings
Katerina Goseva-Popstojanova, Denny Hood, Johann Schumann, Noble Nkwocha
https://arxiv.org/abs/2506.04438

On the Practices of Autonomous Systems Development: Survey-based Empirical Findings
Autonomous systems have gained an important role in many industry domains and are beginning to change everyday life. However, due to dynamically emerging applications and often proprietary constraints, there is a lack of information about the practice of developing autonomous systems. This paper presents the first part of the longitudinal study focused on establishing state-of-the-practice, identifying and quantifying the challenges and benefits, identifying the processes and standards used, an…

Learning Approach to Efficient Vision-based Active Tracking of a Flying Target by an Unmanned Aerial Vehicle
Jagadeswara PKV Pothuri, Aditya Bhatt, Prajit KrisshnaKumar, Manaswin Oddiraju, Souma Chowdhury
https://arxiv.org/abs/2506.18264

Learning Approach to Efficient Vision-based Active Tracking of a Flying Target by an Unmanned Aerial Vehicle
Autonomous tracking of flying aerial objects has important civilian and defense applications, ranging from search and rescue to counter-unmanned aerial systems (counter-UAS). Ground based tracking requires setting up infrastructure, could be range limited, and may not be feasible in remote areas, crowded cities or in dense vegetation areas. Vision based active tracking of aerial objects from another airborne vehicle, e.g., a chaser unmanned aerial vehicle (UAV), promises to fill this important …

NOVA: Navigation via Object-Centric Visual Autonomy for High-Speed Target Tracking in Unstructured GPS-Denied Environments
Alessandro Saviolo, Giuseppe Loianno
https://arxiv.org/abs/2506.18689

NOVA: Navigation via Object-Centric Visual Autonomy for High-Speed Target Tracking in Unstructured GPS-Denied Environments
Autonomous aerial target tracking in unstructured and GPS-denied environments remains a fundamental challenge in robotics. Many existing methods rely on motion capture systems, pre-mapped scenes, or feature-based localization to ensure safety and control, limiting their deployment in real-world conditions. We introduce NOVA, a fully onboard, object-centric framework that enables robust target tracking and collision-aware navigation using only a stereo camera and an IMU. Rather than constructing…

R-CARLA: High-Fidelity Sensor Simulations with Interchangeable Dynamics for Autonomous Racing
Maurice Brunner, Edoardo Ghignone, Nicolas Baumann, Michele Magno
https://arxiv.org/abs/2506.09629

R-CARLA: High-Fidelity Sensor Simulations with Interchangeable Dynamics for Autonomous Racing
Autonomous racing has emerged as a crucial testbed for autonomous driving algorithms, necessitating a simulation environment for both vehicle dynamics and sensor behavior. Striking the right balance between vehicle dynamics and sensor accuracy is crucial for pushing vehicles to their performance limits. However, autonomous racing developers often face a trade-off between accurate vehicle dynamics and high-fidelity sensor simulations. This paper introduces R-CARLA, an enhancement of the CARLA si…

TDACloud: Point Cloud Recognition Using Topological Data Analysis
Anirban Ghosh, Ian Dahlin, Ayan Dutta
https://arxiv.org/abs/2506.18725 https://

TDACloud: Point Cloud Recognition Using Topological Data Analysis
Point cloud-based object/place recognition remains a problem of interest in applications such as autonomous driving, scene reconstruction, and localization. Extracting meaningful local descriptors from a query point cloud that can be matched with the descriptors of the collected point clouds is a challenging problem. Furthermore, when the query point cloud is noisy or has been transformed (e.g., rotated), it adds to the complexity. To this end, we propose a novel methodology, named TDACloud, us…

A workflow for generating synthetic LiDAR datasets in simulation environments
Abhishek Phadke, Shakib Mahmud Dipto, Pratip Rana
https://arxiv.org/abs/2506.17378

A workflow for generating synthetic LiDAR datasets in simulation environments
This paper presents a simulation workflow for generating synthetic LiDAR datasets to support autonomous vehicle perception, robotics research, and sensor security analysis. Leveraging the CoppeliaSim simulation environment and its Python API, we integrate time-of-flight LiDAR, image sensors, and two dimensional scanners onto a simulated vehicle platform operating within an urban scenario. The workflow automates data capture, storage, and annotation across multiple formats (PCD, PLY, CSV), produ…

PG-LIO: Photometric-Geometric fusion for Robust LiDAR-Inertial Odometry
Nikhil Khedekar, Kostas Alexis
https://arxiv.org/abs/2506.18583 https://

PG-LIO: Photometric-Geometric fusion for Robust LiDAR-Inertial Odometry
LiDAR-Inertial Odometry (LIO) is widely used for accurate state estimation and mapping which is an essential requirement for autonomous robots. Conventional LIO methods typically rely on formulating constraints from the geometric structure sampled by the LiDAR. Hence, in the lack of geometric structure, these tend to become ill-conditioned (degenerate) and fail. Robustness of LIO to such conditions is a necessity for its broader deployment. To address this, we propose PG-LIO, a real-time LIO me…

Robots and Children that Learn Together : Improving Knowledge Retention by Teaching Peer-Like Interactive Robots
Imene Tarakli, Samuele Vinanzi, Richard Moore, Alessandro Di Nuovo
https://arxiv.org/abs/2506.18365

Robots and Children that Learn Together : Improving Knowledge Retention by Teaching Peer-Like Interactive Robots
Despite growing interest in Learning-by-Teaching (LbT), few studies have explored how this paradigm can be implemented with autonomous, peer-like social robots in real classrooms. Most prior work has relied on scripted or Wizard-of-Oz behaviors, limiting our understanding of how real-time, interactive learning can be supported by artificial agents. This study addresses this gap by introducing Interactive Reinforcement Learning (RL) as a cognitive model for teachable social robots. We conducted …

EASE: Embodied Active Event Perception via Self-Supervised Energy Minimization
Zhou Chen, Sanjoy Kundu, Harsimran S. Baweja, Sathyanarayanan N. Aakur
https://arxiv.org/abs/2506.17516

EASE: Embodied Active Event Perception via Self-Supervised Energy Minimization
Active event perception, the ability to dynamically detect, track, and summarize events in real time, is essential for embodied intelligence in tasks such as human-AI collaboration, assistive robotics, and autonomous navigation. However, existing approaches often depend on predefined action spaces, annotated datasets, and extrinsic rewards, limiting their adaptability and scalability in dynamic, real-world scenarios. Inspired by cognitive theories of event perception and predictive coding, we p…

Poutine: Vision-Language-Trajectory Pre-Training and Reinforcement Learning Post-Training Enable Robust End-to-End Autonomous Driving
Luke Rowe, Rodrigue de Schaetzen, Roger Girgis, Christopher Pal, Liam Paull
https://arxiv.org/abs/2506.11234

Poutine: Vision-Language-Trajectory Pre-Training and Reinforcement Learning Post-Training Enable Robust End-to-End Autonomous Driving
We present Poutine, a 3B-parameter vision-language model (VLM) tailored for end-to-end autonomous driving in long-tail driving scenarios. Poutine is trained in two stages. To obtain strong base driving capabilities, we train Poutine-Base in a self-supervised vision-language-trajectory (VLT) next-token prediction fashion on 83 hours of CoVLA nominal driving and 11 hours of Waymo long-tail driving. Accompanying language annotations are auto-generated with a 72B-parameter VLM. Poutine is obtained …

Diffusion Models for Safety Validation of Autonomous Driving Systems
Juanran Wang, Marc R. Schlichting, Harrison Delecki, Mykel J. Kochenderfer
https://arxiv.org/abs/2506.08459

Diffusion Models for Safety Validation of Autonomous Driving Systems
Safety validation of autonomous driving systems is extremely challenging due to the high risks and costs of real-world testing as well as the rarity and diversity of potential failures. To address these challenges, we train a denoising diffusion model to generate potential failure cases of an autonomous vehicle given any initial traffic state. Experiments on a four-way intersection problem show that in a variety of scenarios, the diffusion model can generate realistic failure samples while capt…

Towards Biosignals-Free Autonomous Prosthetic Hand Control via Imitation Learning
Kaijie Shi, Wanglong Lu, Hanli Zhao, Vinicius Prado da Fonseca, Ting Zou, Xianta Jiang
https://arxiv.org/abs/2506.08795

Towards Biosignals-Free Autonomous Prosthetic Hand Control via Imitation Learning
Limb loss affects millions globally, impairing physical function and reducing quality of life. Most traditional surface electromyographic (sEMG) and semi-autonomous methods require users to generate myoelectric signals for each control, imposing physically and mentally taxing demands. This study aims to develop a fully autonomous control system that enables a prosthetic hand to automatically grasp and release objects of various shapes using only a camera attached to the wrist. By placing the ha…

DRIVE Through the Unpredictability:From a Protocol Investigating Slip to a Metric Estimating Command Uncertainty
Nicolas Samson, William Larriv\'ee-Hardy, William Dubois, \'Elie Roy-Brouard, Edith Brotherton, Dominic Baril, Julien L\'epine, Fran\c{c}ois Pomerleau
https://arxiv.org/abs/2506.16593

DRIVE Through the Unpredictability:From a Protocol Investigating Slip to a Metric Estimating Command Uncertainty
Off-road autonomous navigation is a challenging task as it is mainly dependent on the accuracy of the motion model. Motion model performances are limited by their ability to predict the interaction between the terrain and the UGV, which an onboard sensor can not directly measure. In this work, we propose using the DRIVE protocol to standardize the collection of data for system identification and characterization of the slip state space. We validated this protocol by acquiring a dataset with two…

VLM-Empowered Multi-Mode System for Efficient and Safe Planetary Navigation
Sinuo Cheng, Ruyi Zhou, Wenhao Feng, Huaiguang Yang, Haibo Gao, Zongquan Deng, Liang Ding
https://arxiv.org/abs/2506.16703

VLM-Empowered Multi-Mode System for Efficient and Safe Planetary Navigation
The increasingly complex and diverse planetary exploration environment requires more adaptable and flexible rover navigation strategy. In this study, we propose a VLM-empowered multi-mode system to achieve efficient while safe autonomous navigation for planetary rovers. Vision-Language Model (VLM) is used to parse scene information by image inputs to achieve a human-level understanding of terrain complexity. Based on the complexity classification, the system switches to the most suitable naviga…

Invariant Extended Kalman Filter for Autonomous Surface Vessels with Partial Orientation Measurements
Derek Benham, Easton Potokar, Joshua G. Mangelson
https://arxiv.org/abs/2506.10850

Invariant Extended Kalman Filter for Autonomous Surface Vessels with Partial Orientation Measurements
Autonomous surface vessels (ASVs) are increasingly vital for marine science, offering robust platforms for underwater mapping and inspection. Accurate state estimation, particularly of vehicle pose, is paramount for precise seafloor mapping, as even small surface deviations can have significant consequences when sensing the seafloor below. To address this challenge, we propose an Invariant Extended Kalman Filter (InEKF) framework designed to integrate partial orientation measurements. While con…

Using Language and Road Manuals to Inform Map Reconstruction for Autonomous Driving
Akshar Tumu, Henrik I. Christensen, Marcell Vazquez-Chanlatte, Chikao Tsuchiya, Dhaval Bhanderi
https://arxiv.org/abs/2506.10317

Using Language and Road Manuals to Inform Map Reconstruction for Autonomous Driving
Lane-topology prediction is a critical component of safe and reliable autonomous navigation. An accurate understanding of the road environment aids this task. We observe that this information often follows conventions encoded in natural language, through design codes that reflect the road structure and road names that capture the road functionality. We augment this information in a lightweight manner to SMERF, a map-prior-based online lane-topology prediction model, by combining structured road…

VAULT: A Mobile Mapping System for ROS 2-based Autonomous Robots
Miguel \'A. Gonz\'alez-Santamarta, Francisco J. Rodr\'iguez-Lera, Vicente Matell\'an-Olivera
https://arxiv.org/abs/2506.09583

VAULT: A Mobile Mapping System for ROS 2-based Autonomous Robots
Localization plays a crucial role in the navigation capabilities of autonomous robots, and while indoor environments can rely on wheel odometry and 2D LiDAR-based mapping, outdoor settings such as agriculture and forestry, present unique challenges that necessitate real-time localization and consistent mapping. Addressing this need, this paper introduces the VAULT prototype, a ROS 2-based mobile mapping system (MMS) that combines various sensors to enable robust outdoor and indoor localization.…

Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving
Haochen Liu, Tianyu Li, Haohan Yang, Li Chen, Caojun Wang, Ke Guo, Haochen Tian, Hongchen Li, Hongyang Li, Chen Lv
https://arxiv.org/abs/2506.09800

Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving
End-to-end autonomous driving has emerged as a promising paradigm for directly mapping sensor inputs to planning maneuvers using learning-based modular integrations. However, existing imitation learning (IL)-based models suffer from generalization to hard cases, and a lack of corrective feedback loop under post-deployment. While reinforcement learning (RL) offers a potential solution to tackle hard cases with optimality, it is often hindered by overfitting to specific driving cases, resulting i…

UAVs Meet Agentic AI: A Multidomain Survey of Autonomous Aerial Intelligence and Agentic UAVs
Ranjan Sapkota, Konstantinos I. Roumeliotis, Manoj Karkee
https://arxiv.org/abs/2506.08045

UAVs Meet Agentic AI: A Multidomain Survey of Autonomous Aerial Intelligence and Agentic UAVs
Agentic UAVs represent a new frontier in autonomous aerial intelligence, integrating perception, decision-making, memory, and collaborative planning to operate adaptively in complex, real-world environments. Driven by recent advances in Agentic AI, these systems surpass traditional UAVs by exhibiting goal-driven behavior, contextual reasoning, and interactive autonomy. We provide a comprehensive foundation for understanding the architectural components and enabling technologies that distinguish…

Ego-centric Learning of Communicative World Models for Autonomous Driving
Hang Wang, Dechen Gao, Junshan Zhang
https://arxiv.org/abs/2506.08149 https://

Ego-centric Learning of Communicative World Models for Autonomous Driving
We study multi-agent reinforcement learning (MARL) for tasks in complex high-dimensional environments, such as autonomous driving. MARL is known to suffer from the \textit{partial observability} and \textit{non-stationarity} issues. To tackle these challenges, information sharing is often employed, which however faces major hurdles in practice, including overwhelming communication overhead and scalability concerns. By making use of generative AI embodied in world model together with its latent …