Nasa's InSight Mars lander is currently trying to endure the abrasive Martian environment, as it sits on the Red Planet conserving power as its solar panels get covered in dust. InSight was designed to be powered by solar energy, gathered through dual two-meter panels. It was always expected that the panels would reduce their power output as time went on and dust landed on them, but would still have enough to last throughout the two-year mission. Unfortunately, not all has gone to plan. Despite InSight landing in Elysium Planitia, a windswept area of Mars that gets lots of sunlight, none of the passing dust devils (funnel-like chimneys of hot air) have been close enough to clean the panels.

Since 2007, two professors at the TU Delft have been researching ways to harvest energy from the wind using a kite. The robotic kite looks set to make its debut in the energy sector, but often inventions are used in unexpected ways. In this series of articles, we take robot innovations from their test-lab and bring them to a randomly selected workplace in the outside world. From kindergarten teacher Fransien, we learn that big kites could also be child's play, quite literally. A robot wheels in the kite and then slowly releases it, painting 8-shaped loops on the sky.

This paper presents stability analysis tools for model predictive control (MPC) with and without terminal weight. Stability analysis of MPC with a limited horizon but without terminal weight is a long-standing open problem. By using a modified value function as an Lyapunov function candidate and the principle of optimality, this paper establishes stability conditions for this type of widely spread MPC algorithms. A new stability guaranteed MPC algorithm without terminal weight (MPCS) is presented. With the help of designing a new sublevel set defined by the value function of one-step ahead stage cost, conditions for checking its recursive feasibility and stability of the proposed MPC algorithm are presented. The new stability condition and the derived MPCS overcome the difficulties arising in the existing terminal weight based MPC framework, including the need of searching a suitable terminal weight and possible poor performance caused by an inappropriate terminal weight. This work is further extended to MPC with a terminal weight for the completeness. Numerical examples are presented to demonstrate the effectiveness of the proposed tool, whereas the existing stability analysis tools are either not applicable or lead to quite conservative results. It shows that the proposed tools offer a number of mechanisms to achieve stability: adjusting state and/or control weights, extending the length of horizon, and adding a simple extra constraint on the first or second state in the optimisation.

For real-world deployments, it is critical to allow robots to navigate in complex environments autonomously. Traditional methods usually maintain an internal map of the environment, and then design several simple rules, in conjunction with a localization and planning approach, to navigate through the internal map. These approaches often involve a variety of assumptions and prior knowledge. In contrast, recent reinforcement learning (RL) methods can provide a model-free, self-learning mechanism as the robot interacts with an initially unknown environment, but are expensive to deploy in real-world scenarios due to inefficient exploration. In this paper, we focus on efficient navigation with the RL technique and combine the advantages of these two kinds of methods into a rule-based RL (RuRL) algorithm for reducing the sample complexity and cost of time. First, we use the rule of wall-following to generate a closed-loop trajectory. Second, we employ a reduction rule to shrink the trajectory, which in turn effectively reduces the redundant exploration space. Besides, we give the detailed theoretical guarantee that the optimal navigation path is still in the reduced space. Third, in the reduced space, we utilize the Pledge rule to guide the exploration strategy for accelerating the RL process at the early stage. Experiments conducted on real robot navigation problems in hex-grid environments demonstrate that RuRL can achieve improved navigation performance.

Traditionally, only large investors had the resources to invest in the land and professional services critical to reaping the benefits of mineral rights investing in the oil and gas market. Today, artificial intelligence (AI) technology has opened that potentially lucrative industry to individuals with as little as $300 at https://www.investinbraneinc.com. Brane Inc.'s cutting-edge technology is using AI to mine the reams of inefficiently collected, paper-based data of the oil well creation process to determine future well locations. As a result, investors of this technology may benefit from the potentially profitable mineral rights investing activities in these new locations. Although the oil and gas industry experiences price fluctuations common to the commodities market, mineral rights investing for oil and gas have historically generated positive returns for deep-pocket investors.

Reinforcement learning is arguably the coolest branch of artificial intelligence. It has already proven its prowess: stunning the world, beating the world champions in games of Chess, Go, and even DotA 2. Using RL for stock trading has always been a holy grail among data scientists. Stock trading has drawn our imaginations because of its ease of access and to misquote Cardi B, we like diamond and we like dollars . There are several ways of using Machine Learning for stock trading. One approach is to use forecasting techniques to predict the movement of the stock and build some heuristic based bot that uses the prediction to make decisions.

An emission-free'nuclear powered' 984ft-long science exploration vessel, as large as the world's longest cruise ship will launch in 2025 with 22 cutting edge laboratories and over 400 people on board. The Earth 300 vessel has been designed to'unite science and exploration to confront Earth's greatest challenges,' according the founder of Iddes Yacht, manufacturer of the ship, Salas Jefferson, who says it will cater for about 160 scientists at one time. The ship will be packed with green technology, a'science sphere', and will be powered by a Molten Salt Reactor, a type of nuclear power generator that uses molten fluoride salts as a coolant and operates at low pressure. When launched, it will act as an'extreme technology platform for science, exploration and innovation at sea', according to Iddes, who say its 22 laboratories will be equipped with robotics and artificial intelligence systems. An emission-free'nuclear powered' 984ft-long science exploration vessel, as large as the world's longest cruise ship will launch in 2025 with 22 cutting edge laboratories and over 400 people on board The Iddes Yacht vessel has been designed to'unite science and exploration to confront Earth's greatest challenges,' according founder Salas Jefferson, who says it will cater for about 160 scientists at one time The ship will be packed with green technology, a'science sphere', and will be powered by a Molten Salt Reactor, a type of nuclear power generator that uses molten fluoride salts as a coolant and operates at low pressure Featuring naval architecture by the NED Project, Earth 300 will introduce'features found on cruise, expedition, research and luxury yachts but she will be none of them,' said Earth 300 chief executive Aaron Olivera. The firm behind the design say it will have a'science city' inside a huge sphere, an observation deck and an interior dedicated to scientific research and expedition.

Sonos's new smaller and cheaper Roam portable speaker is one that won't end up relegated to a drawer collecting dust as it sounds great at home too. The £159 Roam joins the much bigger and heavier £399 Move as the second of firm's battery-powered models and proves itself as one of the best options in a saturated market. The speaker has both wifi and Bluetooth and is triangular in shape, like a Toblerone, but only about the length of a 500ml bottle. It weighs 430g so won't drag down a bag and is easy to grip for carrying about the house. The front is a metal mesh, the back is high-quality mat plastic and the end caps are rubber to help absorb impacts if you drop it.

Uncertainty quantification provides quantitative measures on the reliability of candidate solutions of ill-posed inverse problems. Due to their sequential nature, Monte Carlo sampling methods require large numbers of sampling steps for accurate Bayesian inference and are often computationally infeasible for large-scale inverse problems, such as seismic imaging. Our main contribution is a data-driven variational inference approach where we train a normalizing flow (NF), a type of invertible neural net, capable of cheaply sampling the posterior distribution given previously unseen seismic data from neighboring surveys. To arrive at this result, we train the NF on pairs of low- and high-fidelity migrated images. In our numerical example, we obtain high-fidelity images from the Parihaka dataset and low-fidelity images are derived from these images through the process of demigration, followed by adding noise and migration. During inference, given shot records from a new neighboring seismic survey, we first compute the reverse-time migration image. Next, by feeding this low-fidelity migrated image to the NF we gain access to samples from the posterior distribution virtually for free. We use these samples to compute a high-fidelity image including a first assessment of the image's reliability. To our knowledge, this is the first attempt to train a conditional network on what we know from neighboring images to improve the current image and assess its reliability.

We propose temporally abstract soft actor-critic (TASAC), an off-policy RL algorithm that incorporates closed-loop temporal abstraction into the soft actor-critic (SAC) framework in a simple manner. TASAC adds a second-stage binary policy to choose between the previous action and the action output by an SAC actor. It has two benefits compared to traditional off-policy RL algorithms: persistent exploration and an unbiased multi-step Q operator for TD learning. We demonstrate its advantages over several strong baselines across 5 different categories of 14 continuous control tasks, in terms of both sample efficiency and final performance. Because of its simplicity and generality, TASAC can serve as a drop-in replacement for SAC when temporal abstraction is needed.