Competitive tasks have long served as benchmarks for progress in artificial intelligence. Landmark results have been achieved in domains such as Go [1], poker [2], and real-time strategy games [3], where agents learn to plan, adapt, and compete under structured rules. As research moves from virtual environments to the physical world, robot sports-structured, rule-based competitions involving physical agents-have emerged as a promising frontier for embodied intelligence. Examples include robot soccer [4, 5], table tennis [6, 7], and multi-drone pursuit-evasion [8], which combine high-level strategy with low-level motion control in physically grounded settings. In this paper, we tackle a new embodied competitive task proposed by the V olleyBots testbed [9]: 3v3 multi-drone volleyball. This task exemplifies the structure of a robot sport-well-defined objectives, explicit rules, and head-to-head competition-while presenting a set of unique and underex-plored challenges. Each team must coordinate three quadrotors to rally a ball over a net, switching roles dynamically between offense and defense in a turn-based fashion. The environment is highly dynamic and demands precise timing, agile 3D maneuvering, and strategic team-level behavior. The turn-based nature of ball exchange introduces long-horizon temporal dependencies; the multi-agent setting requires tightly coupled tactics; and the underactuated dynamics of quadrotors call for fine-grained, reactive motor skills.

The Israeli military says a drone launched "from the east" crashed in the southern city of Eilat, causing material damage but no casualties. The drone reportedly fell in the city's hotel zone. Israel has repeatedly conducted its own attacks on Yemen. Following its bombing of Qatar on September 9, Israel intensified its strikes on Yemen, killing dozens. The drone attack in Eilat follows a series of 12 strikes carried out by Israel on Tuesday against Yemen's port of Hodeidah.

How is Russia replenishing its military? What is a'coalition of the willing'? How China forgot promises and'debts' to Ukraine How are Europe, the US pulling apart on Ukraine? Ukraine has worsened fuel shortages across Russia in the past week as it has continued to hit Russia's refineries and energy infrastructure with long-range drones while Poland has called for more oil sanctions in the wake of Russia's first drone attack on NATO soil. In the meantime, Russia's creeping advance resulted in the capture of three villages over the past week, and perhaps for the first time, Ukraine's command reacted by dismissing the retreating officers.

How is Russia replenishing its military? What is a'coalition of the willing'? How China forgot promises and'debts' to Ukraine How are Europe, the US pulling apart on Ukraine? A Ukrainian drone has struck a car in Russia's Belgorod border region, killing one person and injuring another, according to the region's governor. The Ukrainian army lost more than 1,500 troops during front-line fighting over the past day, reported Russia's state TASS news agency, citing the Ministry of Defence.

Collaborative transportation of cable-suspended payloads by teams of Unmanned Aerial Vehicles (UAVs) has the potential to enhance payload capacity, adapt to different payload shapes, and provide built-in compliance, making it attractive for applications ranging from disaster relief to precision logistics. However, multi-UAV coordination under disturbances, nonlinear payload dynamics, and slack--taut cable modes remains a challenging control problem. To our knowledge, no prior work has addressed these cable mode transitions in the multi-UAV context, instead relying on simplifying rigid-link assumptions. We propose CrazyMARL, a decentralized Reinforcement Learning (RL) framework for multi-UAV cable-suspended payload transport. Simulation results demonstrate that the learned policies can outperform classical decentralized controllers in terms of disturbance rejection and tracking precision, achieving an 80% recovery rate from harsh conditions compared to 44% for the baseline method. We also achieve successful zero-shot sim-to-real transfer and demonstrate that our policies are highly robust under harsh conditions, including wind, random external disturbances, and transitions between slack and taut cable dynamics. This work paves the way for autonomous, resilient UAV teams capable of executing complex payload missions in unstructured environments.

Unmanned Aerial Vehicles (UAVs) are increasingly used in automated inspection, delivery, and navigation tasks that require reliable autonomy. This project develops a reinforcement learning (RL) approach to enable a single UAV to autonomously navigate between predefined points without manual intervention. The drone learns navigation policies through trial-and-error interaction, using a custom reward function that encourages goal-reaching efficiency while penalizing collisions and unsafe behavior. The control system integrates ROS with a Gym-compatible training environment, enabling flexible deployment and testing. After training, the learned policy is deployed on a real UAV platform and evaluated under practical conditions. Results show that the UAV can successfully perform autonomous navigation with minimal human oversight, demonstrating the viability of RL-based control for point-to-point drone operations in real-world scenarios.

We investigate the problem of automatic domain generation for the Planning Domain Definition Language (PDDL) using Large Language Models (LLMs), with a particular focus on unmanned aerial vehicle (UAV) tasks. Although PDDL is a widely adopted standard in robotic planning, manually designing domains for diverse applications such as surveillance, delivery, and inspection is labor-intensive and error-prone, which hinders adoption and real-world deployment. To address these challenges, we propose SPAR, a framework that leverages the generative capabilities of LLMs to automatically produce valid, diverse, and semantically accurate PDDL domains from natural language input. To this end, we first introduce a systematically formulated and validated UAV planning dataset, consisting of ground-truth PDDL domains and associated problems, each paired with detailed domain and action descriptions. Building on this dataset, we design a prompting framework that generates high-quality PDDL domains from language input. The generated domains are evaluated through syntax validation, executability, feasibility, and interpretability. Overall, this work demonstrates that LLMs can substantially accelerate the creation of complex planning domains, providing a reproducible dataset and evaluation pipeline that enables application experts without prior experience to leverage it for practical tasks and advance future research in aerial robotics and automated planning.

Abstract--Federated learning (FL) has gained popularity as a privacy-preserving method of training machine learning models on decentralized networks. However to ensure reliable operation of UA V-assisted FL systems, issues like as excessive energy consumption, communication inefficiencies, and security vulnerabilities must be solved. This paper proposes an innovative framework that integrates Digital Twin (DT) technology and Zero-Knowledge Federated Learning (zkFed) to tackle these challenges. UA Vs act as mobile base stations, allowing scattered devices to train FL models locally and upload model updates for aggregation. By incorporating DT technology, our approach enables real-time system monitoring and predictive maintenance, improving UA V network efficiency. Additionally, Zero-Knowledge Proofs (ZKPs) strengthen security by allowing model verification without exposing sensitive data. T o optimize energy efficiency and resource management, we introduce a dynamic allocation strategy that adjusts UA V flight paths, transmission power, and processing rates based on network conditions. Using block coordinate descent and convex optimization techniques, our method significantly reduces system energy consumption by up to 29.6% compared to conventional FL approaches. Simulation results demonstrate improved learning performance, security, and scalability, positioning this framework as a promising solution for next-generation UA V-based intelligent networks. Federated learning (FL) is transforming how machine learning models are trained in distributed networks. Instead of collecting and processing data at a central server, FL allows devices to train models locally and share only the learned parameters. This decentralized approach helps protect user privacy, reduce communication overhead, and improve scalability [1], [2].

Cross-view geo-localization aims to determine the geographical location of a query image by matching it against a gallery of images. This task is challenging due to the significant appearance variations of objects observed from variable views, along with the difficulty in extracting discriminative features. Existing approaches often rely on extracting features through feature map segmentation while neglecting spatial and semantic information. To address these issues, we propose the EVA02-based Multi-scale Frequency Attention Fusion (MFAF) method. The MFAF method consists of Multi-Frequency Branch-wise Block (MFB) and the Frequency-aware Spatial Attention (FSA) module. The MFB block effectively captures both low-frequency structural features and high-frequency edge details across multiple scales, improving the consistency and robustness of feature representations across various viewpoints. Meanwhile, the FSA module adaptively focuses on the key regions of frequency features, significantly mitigating the interference caused by background noise and viewpoint variability. Extensive experiments on widely recognized benchmarks, including University-1652, SUES-200, and Dense-UAV, demonstrate that the MFAF method achieves competitive performance in both drone localization and drone navigation tasks.

How is Russia replenishing its military? What is a'coalition of the willing'? How China forgot promises and'debts' to Ukraine How are Europe, the US pulling apart on Ukraine? Footage shows the moment a Russian Shahed drone struck an administrative building in central Kharkiv in Ukraine, injuring four. Local officials said the drone targeted a busy street to maximize civilian casualties, underscoring relentless strikes as peace efforts stall.