Executing precise and agile flight maneuvers is critical for quadrotors in various applications. Traditional quadrotor control approaches are limited by their reliance on flat trajectories or time-consuming optimization, which restricts their flexibility. Recently, RL-based policy has emerged as a promising alternative due to its ability to directly map observations to actions, reducing the need for detailed system knowledge and actuation constraints. However, a significant challenge remains in bridging the sim-to-real gap, where RL-based policies often experience instability when deployed in real world. In this paper, we investigate key factors for learning robust RL-based control policies that are capable of zero-shot deployment in real-world quadrotors. We identify five critical factors and we develop a PPO-based training framework named SimpleFlight, which integrates these five techniques. We validate the efficacy of SimpleFlight on Crazyflie quadrotor, demonstrating that it achieves more than a 50% reduction in trajectory tracking error compared to state-of-the-art RL baselines. The policy derived by SimpleFlight consistently excels across both smooth polynominal trajectories and challenging infeasible zigzag trajectories on small thrust-to-weight quadrotors. In contrast, baseline methods struggle with high-speed or infeasible trajectories. To support further research and reproducibility, we integrate SimpleFlight into a GPU-based simulator Omnidrones and provide open-source access to the code and model checkpoints. We hope SimpleFlight will offer valuable insights for advancing RL-based quadrotor control. For more details, visit our project website at https://sites.google.com/view/simpleflight/.

We introduce a multi-modal autonomous delivery optimization framework as a coalition game for a fleet of UAVs and ADRs operating in two overlaying networks to address last-mile delivery in urban environments, including high-density areas, road-based routing, and real-world operational challenges. The problem is defined as multiple depot pickup and delivery with time windows constrained over operational restrictions, such as vehicle battery limitation, precedence time window, and building obstruction. Subsequently, the coalition game theory is applied to investigate cooperation structures among the modes to capture how strategic collaboration among vehicles can improve overall routing efficiency. To do so, a generalized reinforcement learning model is designed to evaluate the cost-sharing and allocation to different coalitions for which sub-additive property and non-empty core exist. Our methodology leverages an end-to-end deep multi-agent policy gradient method augmented by a novel spatio-temporal adjacency neighbourhood graph attention network and transformer architecture using a heterogeneous edge-enhanced attention model. Conducting several numerical experiments on last-mile delivery applications, the result from the case study in the city of Mississauga shows that despite the incorporation of an extensive network in the graph for two modes and a complex training structure, the model addresses realistic operational constraints and achieves high-quality solutions compared with the existing transformer-based and heuristics methods and can perform well on non-homogeneous data distribution, generalizes well on the different scale and configuration, and demonstrate a robust performance under stochastic scenarios subject to wind speed and direction.

At first, in mid-November, the mysterious lights were seen blinking across the night skies of New Jersey. Reports of incandescent flying objects were logged in New York, Pennsylvania and Maryland. Bystanders in Virginia Beach said they saw an aircraft "unlike any other they've seen". Sightings have now come from as far afield as Louisiana, Florida and Arizona. People across the US are looking up.

It's been a busy year in cybersecurity, but it's not over yet. This week, we revealed how hackers figured out how to "jailbreak" digital license plates--which are legally issued in at least a couple of states and are valid across the US--allowing them to change the license plate number to basically anything. That means someone with this capability can avoid tolls and tickets, or even change their plate to be the same as their enemy. While the company that makes the plates, Reviver, makes clear that doing this would be both illegal and a terms-of-service violation, we're guessing that the people who want to hide their car's credentials so they can speed all over town aren't too concerned about that. Staff at the Cybersecurity and Infrastructure Security Agency are preparing for an uncertain future.

Ukrainian drones have hit residential buildings in the Russian city of Kazan, more than 1,000 kilometres (600 miles) from the front line, bringing the war deep into the heart of Russia. The Russian Ministry of Defence said the city, located some 800km (500 miles) east of Moscow, was attacked by three waves of drones between 7:40am and 9:20am (04:40 and 06:20 GMT) on Saturday. Eight drones were used in the attack, according to the press service of the Tatarstan regional government. Six hit residential buildings, one hit an industrial facility and another was shot down over a river, it said in a statement. Local authorities said there were no casualties.

An interactive map has revealed a disturbing pattern in drone sightings across the US. An unexplained drone invasion has targeted America's military bases worldwide since October, beginning with a swarm over Langley Air Force Base in Virginia. The pattern became evident when similar activity was reported over New Jersey's Picatinny Arsenal on November 18. Less than one week later, US bases in England and Germany began grappling with incursions by'small unmanned aerial systems.' Back in America sightings were gaining traction. 'Multiple' instances of drones appeared over New Jersey's Navy weapons station, and Ohio's Wright-Patterson Air Force Base closed its airspace due to similar activity on December 13.

Keenan Wyrobek is co-founder and head of product and engineering at Zipline, the world's first drone delivery service whose focus is delivering life-saving medicine to the most difficult to reach places on earth. Prior to Zipline, Keenan was a co-founder and director of the Personal Robotics Program at Willow Garage. He was involved in launching the Robot Operating System (ROS) and shipping PR2, the first personal robot for software R&D. Keenan has spent years delivering high tech products to market across a range of fields including consumer electronics and medical robotics.

Reports of flocks of drones, flying overhead nightly, are coming in from New Jersey down to Maryland. Subscribe to Slate Plus to access ad-free listening to the whole What Next family and all your favorite Slate podcasts. Subscribe today on Apple Podcasts by clicking "Try Free" at the top of our show page. Sign up now at slate.com/whatnextplus to get access wherever you listen.

Congressman Tony Gonzales, R-Texas, discusses the future of drone security in the United States during an appearance on'America Reports.' The Federal Aviation Administration issued more restrictions on drone flights across the Northeast on Friday in response to increased sightings in recent weeks. One day after announcing temporary restrictions on most drone flights in New Jersey, the FAA issued 27 No-Fly Zone notices for "special security reasons" in New York on Friday. The restrictions last through Jan. 18, 2025, and apply to some of the most populated areas in the Empire State, including nearly every NYC borough. The Federal Aviation Administration has issued temporary restrictions on drone flights in 27 areas of New York in response to the influx of reported sightings in recent weeks.

We propose a new simulator, training approach, and policy architecture, collectively called SOUS VIDE, for end-to-end visual drone navigation. Our trained policies exhibit zero-shot sim-to-real transfer with robust real-world performance using only on-board perception and computation. Our simulator, called FiGS, couples a computationally simple drone dynamics model with a high visual fidelity Gaussian Splatting scene reconstruction. FiGS can quickly simulate drone flights producing photorealistic images at up to 130 fps. We use FiGS to collect 100k-300k observation-action pairs from an expert MPC with privileged state and dynamics information, randomized over dynamics parameters and spatial disturbances. We then distill this expert MPC into an end-to-end visuomotor policy with a lightweight neural architecture, called SV-Net. SV-Net processes color image, optical flow and IMU data streams into low-level body rate and thrust commands at 20Hz onboard a drone. Crucially, SV-Net includes a Rapid Motor Adaptation (RMA) module that adapts at runtime to variations in drone dynamics. In a campaign of 105 hardware experiments, we show SOUS VIDE policies to be robust to 30% mass variations, 40 m/s wind gusts, 60% changes in ambient brightness, shifting or removing objects from the scene, and people moving aggressively through the drone's visual field. Code, data, and experiment videos can be found on our project page: https://stanfordmsl.github.io/SousVide/.