Inside Dewar's cavernous whisky warehouses, man's best mechanical friend--a Boston Dynamics robot dog with an ethanol sensor for a nose--is on the hunt for leaky barrels. Wooden barrels are what make the magic happen in your favorite bottle of whisky . At Bacardi Limited, the world's largest privately held spirits company, barrel leakage is a massive headache. Consider the company's Dewar's blended Scotch whisky brand (just one of the dozens it owns). Most of the time, Dewar's will have over 100 warehouses full of aging barrels of whisky, 25,000 casks in each one.

Amazon is reportedly developing'humanoid' robots to pop out of delivery vans to deliver packages, eventually replacing the work of delivery drivers. Amazon is reportedly developing'humanoid' robots to pop out of delivery vans to deliver packages, eventually replacing the work of delivery drivers. O ne of the reasons Amazon is spending billions on robots? They don't need bathroom breaks. Arriving a few minutes early to the public tour of Amazon's hi-tech Stone Mountain, Georgia, warehouse, my request to visit the restroom was met with a resounding no from the security guard in the main lobby.

Treasures unearthed by hundreds of archaeologists so far during work on the controversial planned HS2 train line have been shown exclusively to the BBC. The 450,000 objects, which are being held in a secret warehouse, include a possible Roman gladiator's tag, a hand axe that may be more than 40,000 years old and 19th Century gold dentures. It is an unprecedented amount and array of items, which will yield new insights into Britain's past, says the Centre for British Archaeology. Major building developments in the UK need land to be assessed by archaeologists as part of the planning process, to protect heritage sites. Since 2018 around 1,000 archaeologists have been involved in 60 digs along the route HS2 is set to take between London to Birmingham.

We study the problem of generating arbitrarily large environments to improve the throughput of multi-robot systems. Prior work proposes Quality Diversity (QD) algorithms as an effective method for optimizing the environments of automated warehouses. However, these approaches optimize only relatively small environments, falling short when it comes to replicating real-world warehouse sizes. The challenge arises from the exponential increase in the search space as the environment size increases. Additionally, the previous methods have only been tested with up to 350 robots in simulations, while practical warehouses could host thousands of robots. In this paper, instead of optimizing environments, we propose to optimize Neural Cellular Automata (NCA) environment generators via QD algorithms. We train a collection of NCA generators with QD algorithms in small environments and then generate arbitrarily large environments from the generators at test time. We show that NCA environment generators maintain consistent, regularized patterns regardless of environment size, significantly enhancing the scalability of multi-robot systems in two different domains with up to 2,350 robots. Additionally, we demonstrate that our method scales a single-agent reinforcement learning policy to arbitrarily large environments with similar patterns.

Ukraine's health supplies hit in series of Russian strikes on medical warehouses Warehouses supplying the vast majority of Ukraine's pharmacies have been destroyed in a series of Russian attacks over recent months. Medical supplies worth about $200m (£145m) were destroyed in just two strikes in December and October. A large warehouse storing medicines in the city of Dnipro was destroyed in a Russian air strike on 6 December. As a result, about $110m worth of medicines were destroyed - estimated at up to 30% of Ukraine's monthly supply. It was a missile and drone strike against our facility.

The rapid growth of e-commerce in recent years has significantly transformed people's shopping habits [1]. Consumers increasingly favor online shopping over in-person purchases, leading to a substantial impact on product logistics, which plays a crucial role in customer satisfaction. In addition to product quality and other factors, the timely delivery of orders has become a key determinant of customer satisfaction. Picking and replenishment tasks are responsible for 65% of operating costs [2]. In a conventional manual order picking system, often referred to as a picker-to-parts system, pickers dedicate 70% of their working time to searching for items and traveling within the facility [3, 4].

In Russia's'blitz' of Ukraine, the question of appeasement is back Following another week of intensive and lethal Russian bombardment of Ukraine's cities, a composite image has been doing the rounds on Ukrainian social media. Underneath an old, black-and-white photo of Londoners queuing at a fruit and vegetable stall surrounded by the bombed-out rubble of the Blitz, a second image - this time in colour - creates a striking juxtaposition. Taken on Saturday, it shows shoppers thronging to similar stalls in a northern suburb of the Ukrainian capital, Kyiv, while a column of black smoke rises ominously in the background. Bombs can't stop markets, reads the caption linking the two images. The night before, as the city's sleep was interrupted once again by the now all-too-familiar booms of missile and drone strikes, two people were killed and nine others injured.

The 1,400-year-old'bingo' is the oldest known facility of its kind. Breakthroughs, discoveries, and DIY tips sent every weekday. Archaeologists have discovered South Korea's earliest known ice storage chamber at the site of one of the nation's most historically significant royal castles. At over 1,400 years old, the underground facility offers an unprecedented look into feudal Korean culture's architectural complexities and advancements. Researchers uncovered the ice storage bunker while conducting the seventeenth excavation survey of Busosanseong Fortress located about 90 miles south of Seoul in South Chungcheong Province.

Autonomous navigation in unfamiliar environments requires robots to simultaneously explore, localise, and plan under uncertainty, without relying on predefined maps or extensive training. We present a biologically inspired, Active Inference-based framework, Active Inference MAPping and Planning (AIMAPP). This model unifies mapping, localisation, and decision-making within a single generative model. Inspired by hippocampal navigation, it uses topological reasoning, place-cell encoding, and episodic memory to guide behaviour. The agent builds and updates a sparse topological map online, learns state transitions dynamically, and plans actions by minimising Expected Free Energy. This allows it to balance goal-directed and exploratory behaviours. We implemented a ROS-compatible navigation system that is sensor and robot-agnostic, capable of integrating with diverse hardware configurations. It operates in a fully self-supervised manner, is resilient to drift, and supports both exploration and goal-directed navigation without any pre-training. We demonstrate robust performance in large-scale real and simulated environments against state-of-the-art planning models, highlighting the system's adaptability to ambiguous observations, environmental changes, and sensor noise. The model offers a biologically inspired, modular solution to scalable, self-supervised navigation in unstructured settings. AIMAPP is available at https://github.com/decide-ugent/AIMAPP.