Robot builds a robot's brain: AI generated drone command and control station hosted in the sky Abstract --Advances in artificial intelligence (AI) including large language models (LLMs) and hybrid reasoning models present an opportunity to reimagine how autonomous robots such as drones are designed, developed, and validated. Here, we demonstrate a fully AI-generated drone control system: with minimal human input, an artificial intelligence (AI) model authored all the code for a real-time, self-hosted drone command and control platform, which was deployed and demonstrated on a real drone in flight as well as a simulated virtual drone in the cloud. The system enables real-time mapping, flight telemetry, autonomous mission planning and execution, and safety protocols--all orchestrated through a web interface hosted directly on the drone itself. Not a single line of code was written by a human. We quantitatively benchmark system performance, code complexity, and development speed against prior, human-coded architectures, finding that AI-generated code can deliver functionally complete command-and-control stacks at orders-of-magnitude faster development cycles, though with identifiable current limitations related to specific model context window and reasoning depth. This work sets a precedent for the autonomous creation of robot control systems and, more broadly, suggests a new paradigm for robotics engineering--one in which future robots may be largely co-designed, developed, and verified by artificial intelligence. In this initial work, a robot built a robot's brain. INTRODUCTION In Arnold Schwarzenegger's Terminator, the robots become self-aware and take over the world. In this paper, we take a first step in that direction: A robot (AI code writing machine) creates, from scratch, with minimal human input, the brain of another robot, a drone. Man vs. machine Legend has it that, in the 1870s, a human rail layer (John Henry) tried to beat a steam engine rail laying machine (robot) (Figure 1A). He died trying to beat the machine (robot). John Henry is an American legend and icon, similar to Johny Appleseed, Paul Bunyan, and George Washington. The United States Postal Service issued a postage stamp of him in 1996. According to a folk song from 1918, later popularized by Disney, and still sung by American schoolchildren to this day, the American labor legend'John Henry was a mighty man, born with a hammer right in his hand' ( 1). Peter J. Burke is with the Department of Electrical Engineering and Computer Science, University of California, Irvine, CA 92697 USA (e-mail: pburke@uci.edu). In this work, we demonstrate a similar result in robot control software.

When Ukrainian President Volodymyr Zelenskyy said at the end of last month that Ukraine needs 6 billion to fund the production of interceptor drones, setting a target of 1,000 a day, he had his reasons. Having already reshaped the battlefield by doing work once reserved for long-range missiles, field artillery and human intelligence, drones are now fighting Russian drones -- a boon for Ukraine's dwindling stock of air defense missile systems. In the last two months, just one Ukrainian charity supplying aerial interceptor drones says its devices have downed around 1,500 of the drones that Russia has been sending to reconnoiter the battlefield or to bomb Ukraine's towns and cities.

Three people were killed in a Russian attack on the Stepnohirsk community in Ukraine's Zaporizhia region, the local military administration said on Telegram. Russia launched 405 attacks on 10 settlements in the region in the past day, the administration said on Monday. Russian drone attacks killed three people in the Chuhuiv district of Ukraine's Kharkiv region, the regional prosecutor's office said. The victims included a man killed when Russian drones caused a fire in his home in the village of Losivka, and a man and a woman who were riding a motorcycle when they were killed. The prosecutor's office said it was investigating the motorcycle attack as a possible war crime.

We present VLH, a novel Visual-Language-Haptic Foundation Model that unifies perception, language, and tactile feedback in aerial robotics and virtual reality. Unlike prior work that treats haptics as a secondary, reactive channel, VLH synthesizes mid-air force and vibration cues as a direct consequence of contextual visual understanding and natural language commands. Our platform comprises an 8-inch quadcopter equipped with dual inverse five-bar linkage arrays for localized haptic actuation, an egocentric VR camera, and an exocentric top-down view. Visual inputs and language instructions are processed by a fine-tuned OpenVLA backbone - adapted via LoRA on a bespoke dataset of 450 multimodal scenarios - to output a 7-dimensional action vector (Vx, Vy, Vz, Hx, Hy, Hz, Hv). INT8 quantization and a high-performance server ensure real-time operation at 4-5 Hz. In human-robot interaction experiments (90 flights), VLH achieved a 56.7% success rate for target acquisition (mean reach time 21.3 s, pose error 0.24 m) and 100% accuracy in texture discrimination. Generalization tests yielded 70.0% (visual), 54.4% (motion), 40.0% (physical), and 35.0% (semantic) performance on novel tasks. These results demonstrate VLH's ability to co-evolve haptic feedback with perceptual reasoning and intent, advancing expressive, immersive human-robot interactions.

Energy-Predictive Planning for Optimizing Drone Service Delivery Guanting Ren, Babar Shahzaad, Balsam Alkouz, Abdallah Lakhdari, Ath-man Bouguettaya An Energy-Predictive Drone Service (EPDS) framework to minimize the average delivery time. A heuristic-based optimization for drone services composition to reduce recharging and waiting time. Abstract We propose a novel Energy-Predictive Drone Service (EPDS) framework for efficient package delivery within a skyway network. The EPDS framework incorporates a formal modeling of an EPDS and an adaptive bidirectional Long Short-Term Memory (Bi-LSTM) machine learning model. This model predicts the energy status and stochastic arrival times of other drones operating in the same skyway network. Leveraging these predictions, we develop a heuristic optimization approach for composite drone services. This approach identifies the most time-efficient and energy-efficient skyway path and recharging schedule for each drone in the network. We conduct extensive experiments using a real-world drone flight dataset to evaluate the performance of the proposed framework. Introduction The Internet of Things (IoT) has become more mature and widespread, largely thanks to advancements in software and hardware technologies. Drones serve various purposes, including aiding in farm irrigation, capturing aerial imagery for entertainment, and facilitating the delivery of retail goods (Mohsan et al. (2023)). Drone delivery services are increasingly important because they can offer faster delivery times, lower operational costs, and potentially a greener alternative to traditional delivery methods (Eskandaripour and Boldsaikhan (2023)). Several key challenges, however, hinder the wider adoption of drones for delivery services (Sah et al. (2021)). A primary challenge is constrained battery capacity, which limits a drone's flight range (Huang et al. (2022)). With current lightweight batteries, delivery drones are not well-suited for long-distance trips, particularly when carrying heavy payloads. As a result, some studies propose using drones only for last-mile deliveries (Garg et al. (2023)). Despite these limitations, drones remain a clean, cost-effective, and ubiquitous alternative to land-based delivery in both urban and rural areas (Attenni et al. (2023)).

How long does it take to identify the helmet of a hiker lost in a 183-hectare mountain area, analyzing 2,600 frames taken by a drone from approximately 50 meters away? If done with a human eye, weeks or months. If analyzed by an artificial intelligence system, one afternoon. The National Alpine and Speleological Rescue Corps, known by it's Italian initialism CNSAS, relied on AI to find the body of a person missing in Italy's Piedmont region on the north face of Monviso--the highest peak in the Cottian Alps--since September 2024. According to Saverio Isola, the CNSAS drone pilot who intervened along with his colleague Giorgio Viana, the operation--including searching for any sign of the missing hiker, the discovery and recovery of his body, and a stoppage due to bad weather--lasted less than three days.

Generative Adversarial Networks (GANs) have achieved realistic super-resolution (SR) of images however, they lack semantic consistency and per-pixel confidence, limiting their credibility in critical remote sensing applications such as disaster response, urban planning and agriculture. This paper introduces Semantic and Uncertainty-Aware ESRGAN (SU-ESRGAN), the first SR framework designed for satellite imagery to integrate the ESRGAN, segmentation loss via DeepLabv3 for class detail preservation and Monte Carlo dropout to produce pixel-wise uncertainty maps. The SU-ESRGAN produces results (PSNR, SSIM, LPIPS) comparable to the Baseline ESRGAN on aerial imagery. This novel model is valuable in satellite systems or UAVs that use wide field-of-view (FoV) cameras, trading off spatial resolution for coverage. The modular design allows integration in UAV data pipelines for on-board or post-processing SR to enhance imagery resulting due to motion blur, compression and sensor limitations. Further, the model is fine-tuned to evaluate its performance on cross domain applications. The tests are conducted on two drone based datasets which differ in altitude and imaging perspective. Performance evaluation of the fine-tuned models show a stronger adaptation to the Aerial Maritime Drone Dataset, whose imaging characteristics align with the training data, highlighting the importance of domain-aware training in SR-applications.

Unmanned Aerial Vehicle (UAV) Vision-Language Navigation (VLN) aims to enable agents to accurately localize targets and plan flight paths in complex environments based on natural language instructions, with broad applications in intelligent inspection, disaster rescue, and urban monitoring. Recent progress in Vision-Language Models (VLMs) has provided strong semantic understanding for this task, while reinforcement learning (RL) has emerged as a promising post-training strategy to further improve generalization. However, existing RL methods often suffer from inefficient use of training data, slow convergence, and insufficient consideration of the difficulty variation among training samples, which limits further performance improvement. To address these challenges, we propose \textbf{Semantic-Aware Gaussian Curriculum Scheduling (SA-GCS)}, a novel training framework that systematically integrates Curriculum Learning (CL) into RL. SA-GCS employs a Semantic-Aware Difficulty Estimator (SA-DE) to quantify the complexity of training samples and a Gaussian Curriculum Scheduler (GCS) to dynamically adjust the sampling distribution, enabling a smooth progression from easy to challenging tasks. This design significantly improves training efficiency, accelerates convergence, and enhances overall model performance. Extensive experiments on the CityNav benchmark demonstrate that SA-GCS consistently outperforms strong baselines across all metrics, achieves faster and more stable convergence, and generalizes well across models of different scales, highlighting its robustness and scalability. The implementation of our approach is publicly available.

A Russian attack killed three people in Ukraine's southeastern Zaporizhia region on Sunday, Governor Ivan Fedorov wrote on Telegram. A Ukrainian drone attack sparked a major fire at an oil depot in Sochi in southern Russia, the governor of Russia's Krasnodar region, Veniamin Kondratiev, said on Sunday. The fire was extinguished hours later after 120 firefighters were deployed, officials said. Russia's civil aviation authority, Rosaviatsia, briefly halted flights at Sochi's airport during the fire. Ukraine's military says it used drones to target several sites inside Russia, including refineries, an airfield and an electronics plant.

An overnight Ukrainian drone attack has sparked a fire at an oil depot in Sochi, the southwestern Russian resort that hosted the 2014 Winter Olympic Games, local authorities say. The attack came a day after Ukraine's military said it struck the Ryazan oil refinery in central Russia, causing a fire. Ukraine has regularly hit Russian oil and gas infrastructure in response to attacks on its own territory since Russia began its war in February 2022. "Sochi suffered a drone attack by the Kyiv regime last night," the governor of Russia's Krasnodar region, Veniamin Kondratiev, said on the Telegram messaging application on Sunday. Drone wreckage hit an "oil tank, which caused a fire", Kondratiev said.