Results


DARPA CODE Autonomy Engine Demonstrated on Avenger UAS

#artificialintelligence

General Atomics Aeronautical Systems, Inc. (GA-ASI) has demonstrated the DARPA-developed Collaborative Operations in Denied Environment (CODE) autonomy engine on the company's Avenger Unmanned Aircraft System (UAS). CODE was used in order to gain further understanding of cognitive Artificial Intelligence (AI) processing on larger UAS platforms for air-to-air targeting. Using a network-enabled Tactical Targeting Network Technology (TTNT) radio for mesh network mission communications, GA-ASI was able to demonstrate integration of emerging Advanced Tactical Data Links (ATDL), as well as separation between flight and mission critical systems. During the autonomous flight, CODE software controlled the manoeuvring of the Avenger UAS for over two hours without human pilot input. GA-ASI extended the base software behavioural functions for a coordinated air-to-air search with up to six aircraft, using five virtual aircraft for the purposes of the demonstration.


AI-Powered Sensing Technology to be Developed for MQ-9 UAS

#artificialintelligence

General Atomics Aeronautical Systems, Inc. (GA-ASI) has been awarded a contract by the U.S. Department of Defense's Joint Artificial Intelligence Center (JAIC) to develop enhanced autonomous sensing capabilities for unmanned aerial vehicles (UAVs). The JAIC Smart Sensor project aims to advance drone-based AI technology by demonstrating object recognition algorithms and employing onboard AI to automatically control UAV sensors and direct autonomous flight. GA-ASI will deploy these new capabilities on a MQ-9 Reaper UAV equipped with a variety of sensors, including GA-ASI's Reaper Defense Electronic Support System (RDESS) and Lynx Synthetic Aperture Radar (SAR). GA-ASI's Metis Intelligence, Surveillance and Reconnaissance (ISR) tasking and intelligence-sharing application, which enables operators to specify effects-based mission objectives and receive automatic notification of actionable intelligence, will be used to command the unmanned aircraft. J.R. Reid, GA-ASI Vice President of Strategic Development, commented: "GA-ASI is excited to leverage the considerable investment we have made to advance the JAIC's autonomous sensing objective. This will bring a tremendous increase in unmanned systems capabilities for applications across the full-range of military operations."


'Machines set loose to slaughter': the dangerous rise of military AI

#artificialintelligence

Two menacing men stand next to a white van in a field, holding remote controls. They open the van's back doors, and the whining sound of quadcopter drones crescendos. They flip a switch, and the drones swarm out like bats from a cave. In a few seconds, we cut to a college classroom. The students scream in terror, trapped inside, as the drones attack with deadly force. The lesson that the film, Slaughterbots, is trying to impart is clear: tiny killer robots are either here or a small technological advance away. And existing defences are weak or nonexistent.


Drones – the New Critical Infrastructure

#artificialintelligence

Be prepared in the near future when you gaze into the blue skies to perceive a whole series of strange-looking things – no, they will not be birds, nor planes, or even superman. They may be temporarily, and in some cases startlingly mistaken as UFOs, given their bizarre and ominous appearance. But, in due course, they will become recognized as valuable objects of a new era of human-made flying machines, intended to serve a broad range of missions and objectives. Many such applications are already incorporated and well entrenched in serving essential functions for extending capabilities in our vital infrastructures such as transportation, utilities, the electric grid, agriculture, emergency services, and many others. Rapidly advancing technologies have made possible the dramatic capabilities of unmanned aerial vehicles (UAV/drones) to uniquely perform various functions that were inconceivable a mere few years ago.


Deep Learning and Reinforcement Learning for Autonomous Unmanned Aerial Systems: Roadmap for Theory to Deployment

arXiv.org Machine Learning

Unmanned Aerial Systems (UAS) are being increasingly deployed for commercial, civilian, and military applications. The current UAS state-of-the-art still depends on a remote human controller with robust wireless links to perform several of these applications. The lack of autonomy restricts the domains of application and tasks for which a UAS can be deployed. Enabling autonomy and intelligence to the UAS will help overcome this hurdle and expand its use improving safety and efficiency. The exponential increase in computing resources and the availability of large amount of data in this digital era has led to the resurgence of machine learning from its last winter. Therefore, in this chapter, we discuss how some of the advances in machine learning, specifically deep learning and reinforcement learning can be leveraged to develop next-generation autonomous UAS. We first begin motivating this chapter by discussing the application, challenges, and opportunities of the current UAS in the introductory section. We then provide an overview of some of the key deep learning and reinforcement learning techniques discussed throughout this chapter. A key area of focus that will be essential to enable autonomy to UAS is computer vision. Accordingly, we discuss how deep learning approaches have been used to accomplish some of the basic tasks that contribute to providing UAS autonomy. Then we discuss how reinforcement learning is explored for using this information to provide autonomous control and navigation for UAS. Next, we provide the reader with directions to choose appropriate simulation suites and hardware platforms that will help to rapidly prototype novel machine learning based solutions for UAS. We additionally discuss the open problems and challenges pertaining to each aspect of developing autonomous UAS solutions to shine light on potential research areas.


Human-in-the-Loop Methods for Data-Driven and Reinforcement Learning Systems

arXiv.org Artificial Intelligence

Recent successes combine reinforcement learning algorithms and deep neural networks, despite reinforcement learning not being widely applied to robotics and real world scenarios. This can be attributed to the fact that current state-of-the-art, end-to-end reinforcement learning approaches still require thousands or millions of data samples to converge to a satisfactory policy and are subject to catastrophic failures during training. Conversely, in real world scenarios and after just a few data samples, humans are able to either provide demonstrations of the task, intervene to prevent catastrophic actions, or simply evaluate if the policy is performing correctly. This research investigates how to integrate these human interaction modalities to the reinforcement learning loop, increasing sample efficiency and enabling real-time reinforcement learning in robotics and real world scenarios. This novel theoretical foundation is called Cycle-of-Learning, a reference to how different human interaction modalities, namely, task demonstration, intervention, and evaluation, are cycled and combined to reinforcement learning algorithms. Results presented in this work show that the reward signal that is learned based upon human interaction accelerates the rate of learning of reinforcement learning algorithms and that learning from a combination of human demonstrations and interventions is faster and more sample efficient when compared to traditional supervised learning algorithms. Finally, Cycle-of-Learning develops an effective transition between policies learned using human demonstrations and interventions to reinforcement learning. The theoretical foundation developed by this research opens new research paths to human-agent teaming scenarios where autonomous agents are able to learn from human teammates and adapt to mission performance metrics in real-time and in real world scenarios.


Death by drone: How can states justify targeted killings?

Al Jazeera

In a move that caused a ripple effect across the Middle East, Iranian General Qassem Soleimani was killed in a US drone strike near Baghdad's international airport on January 3. On that day, the Pentagon announced the attack was carried out "at the direction of the president". In a new report examining the legality of armed drones and the Soleimani killing in particular, Agnes Callamard, UN special rapporteur on extrajudicial and arbitrary killings, said the US raid that killed Soleimani was "unlawful". Callamard presented her report at the Human Rights Council in Geneva on Thursday. The United States, which is not a member after quitting the council in 2018, rejected the report saying it gave "a pass to terrorists". In Callamard's view, the consequences of targeted killings by armed drones have been neglected by states.


Online Mapping and Motion Planning under Uncertainty for Safe Navigation in Unknown Environments

arXiv.org Artificial Intelligence

Safe autonomous navigation is an essential and challenging problem for robots operating in highly unstructured or completely unknown environments. Under these conditions, not only robotic systems must deal with limited localisation information, but also their manoeuvrability is constrained by their dynamics and often suffer from uncertainty. In order to cope with these constraints, this manuscript proposes an uncertainty-based framework for mapping and planning feasible motions online with probabilistic safety-guarantees. The proposed approach deals with the motion, probabilistic safety, and online computation constraints by: (i) incrementally mapping the surroundings to build an uncertainty-aware representation of the environment, and (ii) iteratively (re)planning trajectories to goal that are kinodynamically feasible and probabilistically safe through a multi-layered sampling-based planner in the belief space. In-depth empirical analyses illustrate some important properties of this approach, namely, (a) the multi-layered planning strategy enables rapid exploration of the high-dimensional belief space while preserving asymptotic optimality and completeness guarantees, and (b) the proposed routine for probabilistic collision checking results in tighter probability bounds in comparison to other uncertainty-aware planners in the literature. Furthermore, real-world in-water experimental evaluation on a non-holonomic torpedo-shaped autonomous underwater vehicle and simulated trials in the Stairwell scenario of the DARPA Subterranean Challenge 2019 on a quadrotor unmanned aerial vehicle demonstrate the efficacy of the method as well as its suitability for systems with limited on-board computational power.


Ten Ways the Precautionary Principle Undermines Progress in Artificial Intelligence

#artificialintelligence

Artificial intelligence (AI) has the potential to deliver significant social and economic benefits, including reducing accidental deaths and injuries, making new scientific discoveries, and increasing productivity.[1] However, an increasing number of activists, scholars, and pundits see AI as inherently risky, creating substantial negative impacts such as eliminating jobs, eroding personal liberties, and reducing human intelligence.[2] Some even see AI as dehumanizing, dystopian, and a threat to humanity.[3] As such, the world is dividing into two camps regarding AI: those who support the technology and those who oppose it. Unfortunately, the latter camp is increasingly dominating AI discussions, not just in the United States, but in many nations around the world. There should be no doubt that nations that tilt toward fear rather than optimism are more likely to put in place policies and practices that limit AI development and adoption, which will hurt their economic growth, social ...