Lamborghini's new hybrid supercar includes a three-level drift mode and three axial flux motors The supercar pulls out the stops with a screaming 10,000 revolutions per minute at the redline. With a top speed of 213 miles per hour and a 10,000 rpm redline, the Lamborghini Temerario is a wild machine. Breakthroughs, discoveries, and DIY tips sent every weekday. Lamborghini's legacy gas-only machines have been unapologetically loud, brash, and in your face with sonorous symphonies conducted by fuel-guzzling V12 and V10 engines. Today, the brand is in its electrification age, with three plug-in hybrids: the Urus SE SUV, the top-tier Revuelto, and the newest Raging Bull, the Temerario.

Abstract-- T o support the circular economy, robotic systems must not only assemble new products but also disassemble end-of-life (EOL) ones for reuse, recycling, or safe disposal. Existing approaches to disassembly sequence planning often assume deterministic and fully observable product models, yet real EOL products frequently deviate from their initial designs due to wear, corrosion, or undocumented repairs. We argue that disassembly should therefore be formulated as a Partially Observable Markov Decision Process (POMDP), which naturally captures uncertainty about the product's internal state. We present a mathematical formulation of disassembly as a POMDP, in which hidden variables represent uncertain structural or physical properties. Building on this formulation, we propose a task and motion planning framework that automatically derives specific POMDP models from CAD data, robot capabilities, and inspection results. T o obtain tractable policies, we approximate this formulation with a reinforcement-learning approach that operates on stochastic action outcomes informed by inspection priors, while a Bayesian filter continuously maintains beliefs over latent EOL conditions during execution. Using three products on two robotic systems, we demonstrate that this probabilistic planning framework outperforms deterministic baselines in terms of average disassembly time and variance, generalizes across different robot setups, and successfully adapts to deviations from the CAD model, such as missing or stuck parts. I. INTRODUCTION Modern industrial production still follows a linear model of make-use-dispose, accelerating the depletion of natural resources on our planet.

A robotics company has advanced from a backflipping robot to a side-flipping robot. Robots aren't just efficient machines anymore, they are now agile performers that can flip and jog. Take, for instance, Unitree, a Chinese robotics company that has been making headlines with its incredible G1 humanoid robot. You might have seen it dancing alongside humans or remembered its predecessor, the H1, which stunned us with a backflip using electric motors. But now, the G1 has taken things to a whole new level.

The Bletchley Park artificial intelligence summit in 2023 was a landmark event in AI regulation simply by virtue of its existence. Between the event's announcement and its first day, the mainstream conversation had changed from a tone of light bafflement to a general agreement that AI regulation may be worth discussing. However, the task for its follow-up, held at a research park on the outskirts of Seoul this week, is harder: can the UK and South Korea show that governments are moving from talking about AI regulation to actually delivering it? At the end of the Seoul summit, the big achievement the UK was touting was the creation of a global network of AI safety institutes, building on the British trailblazers founded after the last meeting. The technology secretary, Michelle Donelan, attributed the new institutes to the "Bletchley effect" in action, and announced plans to lead a system whereby regulators in the US, Canada, Britain, France, Japan, Korea, Australia, Singapore and the EU share information about AI models, harms and safety incidents.

The functional replication and actuation of complex structures inspired by nature is a longstanding goal for humanity. Creating such complex structures combining soft and rigid features and actuating them with artificial muscles would further our understanding of natural kinematic structures. We printed a biomimetic hand in a single print process comprised of a rigid skeleton, soft joint capsules, tendons, and printed touch sensors. We showed it's actuation using electric motors. In this work, we expand on this work by adding a forearm that is also closely modeled after the human anatomy and replacing the hand's motors with 22 independently controlled pneumatic artificial muscles (PAMs). Our thin, high-strain (up to 30.1%) PAMs match the performance of state-of-the-art artificial muscles at a lower cost. The system showcases human-like dexterity with independent finger movements, demonstrating successful grasping of various objects, ranging from a small, lightweight coin to a large can of 272g in weight. The performance evaluation, based on fingertip and grasping forces along with finger joint range of motion, highlights the system's potential.

World Endurance Championship (WEC) racing events are characterised by a relevant performance gap among competitors. The fastest vehicles category, consisting in hybrid vehicles, has to respect energy usage constraints set by the technical regulation. Considering absence of competitors, i.e. traffic conditions, the optimal energy usage strategy for lap time minimisation is typically computed through a constrained optimisation problem. To the best of our knowledge, the majority of state-of-the-art works neglects competitors. This leads to a mismatch with the real world, where traffic generates considerable time losses. To bridge this gap, we propose a new framework to offline compute optimal strategies for the powertrain energy management considering competitors. Through analysis of the available data from previous events, statistics on the sector times and overtaking probabilities are extracted to encode the competitors' behaviour. Adopting a multi-agent model, the statistics are then used to generate realistic Monte Carlo (MC) simulation of their position along the track. The simulator is then adopted to identify the optimal strategy as follows. We develop a longitudinal vehicle model for the ego-vehicle and implement an optimisation problem for lap time minimisation in absence of traffic, based on Genetic Algorithms. Solving the optimisation problem for a variety of constraints generates a set of candidate optimal strategies. Stochastic Dynamic Programming is finally implemented to choose the best strategy considering competitors, whose motion is generated by the MC simulator. Our approach, validated on data from a real stint of race, allows to significantly reduce the lap time.

The series, which is produced by BBC StoryWorks Commercial Productions and presented by the International Science Council (ISC), includes films, articles and podcasts which will be hosted on a dedicated BBC.com StoryWorks webpage. The series explores how scientific culture is changing for the better, towards a future of more effective and inclusive citizen engagement, interdisciplinary and international cooperation, and open knowledge-sharing. This five-minute film highlights the innovative use of shipwrecks to map the seabed to inform the siting of offshore windfarms as the seas around Ireland provide an abundance of wind resources. Shipwrecks disturb near-seabed currents, causing certain types of sediments to be washed away or eroded. By studying these changes, we can better predict how man-made structures including wind turbines, will behave on the seabed over time.

Bittle is a small but agile and powerful robot dog. It is an ideal tool for learning, teaching and researching quadruped robots. With customized Arduino board coordinating all instinctive and sophisticated movements, you can clip on various sensors to bring in perception. You could also inject artificial intelligence capabilities by mounting a Raspberry Pi or other AI chips. Bittle skeleton consist of 3D interlocking parts,which reduces the use of screws and makes it simple and beautiful.

Now that the car is evolving into essentially a smartphone on wheels, it's no wonder Apple is kicking the tires. First, there is the transition from internal combustion engines to electric motors, which have far fewer mechanical parts. Now, enabled by that change, a second shift is under way--one that's a prerequisite for a self-driving future. For a century, the automobile was a system of interoperating mechanics: engine, transmission, drive shaft, brakes, etc. As those mechanics evolved, electronic sensors and processors were brought in to assist them, but the concepts changed little.

Hydraulics are sometimes looked at as an alternative to electric motors. Hydraulic systems use an incompressible liquid (as opposed to pneumatics that use a compressible gas) to transfer force from one place to another. Since the hydraulic system will be a closed system (ignore relief valves for now) when you apply a force to one end of the system that force is transferred to another part of that system. By manipulating the volume of fluid in different parts of the system you can change the forces in different parts of the system (Remember Pascal's Law from high school??). So here are some of the basic components used (or needed) to develop a hydraulic system.