Exploring planetary bodies with lower gravity, such as the moon and Mars, allows legged robots to utilize jumping as an efficient form of locomotion thus giving them a valuable advantage over traditional rovers for exploration. Motivated by this fact, this paper presents the design, simulation, and learning-based "in-flight" attitude control of Olympus, a jumping legged robot tailored to the gravity of Mars. First, the design requirements are outlined followed by detailing how simulation enabled optimizing the robot's design - from its legs to the overall configuration - towards high vertical jumping, forward jumping distance, and in-flight attitude reorientation. Subsequently, the reinforcement learning policy used to track desired in-flight attitude maneuvers is presented. Successfully crossing the sim2real gap, extensive experimental studies of attitude reorientation tests are demonstrated.

We introduce Olympus, a new approach that transforms Multimodal Large Language Models (MLLMs) into a unified framework capable of handling a wide array of computer vision tasks. Utilizing a controller MLLM, Olympus delegates over 20 specialized tasks across images, videos, and 3D objects to dedicated modules. This instruction-based routing enables complex workflows through chained actions without the need for training heavy generative models. Olympus easily integrates with existing MLLMs, expanding their capabilities with comparable performance. Experimental results demonstrate that Olympus achieves an average routing accuracy of 94.75% across 20 tasks and precision of 91.82% in chained action scenarios, showcasing its effectiveness as a universal task router that can solve a diverse range of computer vision tasks. Project page: http://yuanze-lin.me/Olympus_page/

Minecraft, the best-selling video game of all time, has been around for more than a decade. The procedurally generated survival sandbox is constantly evolving, playing host to everything from speedrun challenges and political dramas to lessons. But it's best known as digital Lego-- and it's seen some incredible creations over the years. For most, it's a time-consuming hobby, but a few have parlayed their passion into a professional career. Here are some of the most spectacular Minecraft creations that took years to build.

Research challenges encountered across science, engineering, and economics can frequently be formulated as optimization tasks. In chemistry and materials science, recent growth in laboratory digitization and automation has sparked interest in optimization-guided autonomous discovery and closed-loop experimentation. Experiment planning strategies based on off-the-shelf optimization algorithms can be employed in fully autonomous research platforms to achieve desired experimentation goals with the minimum number of trials. However, the experiment planning strategy that is most suitable to a scientific discovery task is a priori unknown while rigorous comparisons of different strategies are highly time and resource demanding. As optimization algorithms are typically benchmarked on low-dimensional synthetic functions, it is unclear how their performance would translate to noisy, higher-dimensional experimental tasks encountered in chemistry and materials science. We introduce Olympus, a software package that provides a consistent and easy-to-use framework for benchmarking optimization algorithms against realistic experiments emulated via probabilistic deep-learning models. Olympus includes a collection of experimentally derived benchmark sets from chemistry and materials science and a suite of experiment planning strategies that can be easily accessed via a user-friendly python interface. Furthermore, Olympus facilitates the integration, testing, and sharing of custom algorithms and user-defined datasets. In brief, Olympus mitigates the barriers associated with benchmarking optimization algorithms on realistic experimental scenarios, promoting data sharing and the creation of a standard framework for evaluating the performance of experiment planning strategies

Despite denying persistent rumors that it would exit the camera business, Olympus is doing exactly that. The company has announced that will sell its camera business to Japan Industrial Partners (JIP), the same company that purchased Sony's VAIO PC division (via The Verge). Olympus will now focus on its much larger business supplying industrial and medical imaging equipment. Olympus said it improved cost structure, focused on high-profit cameras and lenses and took other steps to "cope with the extremely severe digital camera market." Despite those efforts, however, the company said it "recorded operating losses for three consecutive fiscal years up to the term ended in March 2020."

While you were weekending, you might have missed Roku's cheap, entry-level video streamer, our first 24 hours with Olympus' intriguing new camera and Samsung's attempts to hype up its next smartphone way in advance. The time is now for cheap set-top boxesReview: Roku's new $30 player is more intriguing than its high-end siblings The Roku Express is a streaming marvel thanks to its low price. If you can live with some speed issues, it's perfect for bringing streaming video to screens all over your house -- and could well be your first set-top box. The Mark II is all about speed, image stabilization -- and heft24 hours with Olympus' new OM-D E-M1 Mark II After a day of using Olympus' OM-D E-M1 Mark II, Edgar Alvarez says capturing moving subjects is a breeze, especially compared to its aging predecessor. He's crediting the performance to the new AF system and improved tracking performance.