The RoboCup Logistics League is a RoboCup competition in a smart factory scenario that has focused on task planning, job scheduling, and multi-agent coordination. The focus on production logistics allowed teams to develop highly competitive strategies, but also meant that some recent developments in the context of smart manufacturing are not reflected in the competition, weakening its relevance over the years. In this paper, we describe the vision for the RoboCup Smart Manufacturing League, a new competition designed as a larger smart manufacturing scenario, reflecting all the major aspects of a modern factory. It will consist of several tracks that are initially independent but gradually combined into one smart manufacturing scenario. The new tracks will cover industrial robotics challenges such as assembly, human-robot collaboration, and humanoid robotics, but also retain a focus on production logistics. We expect the reenvisioned competition to be more attractive to newcomers and well-tried teams, while also shifting the focus to current and future challenges of industrial robotics.

An artificial intelligence model from Microsoft can recreate realistic video game footage that the company says could help designers make games, but experts are unconvinced that the tool will be useful for most game developers. Neural networks that can produce coherent and accurate footage from video games are not new. A recent Google-created AI generated a fully playable version of the classic computer game Doom without access to the underlying game engine. The original Doom, however, was released in 1993; more modern games are far more complex, with sophisticated physics and computationally intensive graphics, which have proved trickier for AIs to faithfully recreate. Google creates self-replicating life from digital'primordial soup' Now, Katja Hofmann at Microsoft Research and her colleagues have developed an AI model called Muse, which can recreate full sequences of the multiplayer online battle game Bleeding Edge. These sequences appear to obey the game's underlying physics and keep players and in-game objects consistent over time, which implies that the model has grasped a deep understanding of the game, says Hofmann.

The annual CES Las Vegas show is a famous testing ground for some of the slightly wackier health tech ideas. This year's is no exception, with developers bringing forward a range of products that could change how many people live their lives. Many of these products are now in the final stages of development and being released if not already available on early access. But a number also remain largely conceptual prototypes, such as a mirror for scanning someone for body fat percentage, which its presenter said was still very much just an example of what they could do. It sounds like something out of a sci-fi movie, but developers have designed a computer mouse controlled entirely by your tongue.

Angle-resolved photoemission spectroscopy (ARPES) is a powerful experimental technique to determine the electronic structure of solids. Advances in light sources for ARPES experiments are currently leading to a vast increase of data acquisition rates and data quantity. On the other hand, access time to the most advanced ARPES instruments remains strictly limited, calling for fast, effective, and on-the-fly data analysis tools to exploit this time. In response to this need, we introduce ARPESNet, a versatile autoencoder network that efficiently summmarises and compresses ARPES datasets. We train ARPESNet on a large and varied dataset of 2-dimensional ARPES data extracted by cutting standard 3-dimensional ARPES datasets along random directions in $\mathbf{k}$. To test the data representation capacity of ARPESNet, we compare $k$-means clustering quality between data compressed by ARPESNet, data compressed by discrete cosine transform, and raw data, at different noise levels. ARPESNet data excels in clustering quality despite its high compression ratio.

A robot's actions are inherently stochastic, as its sensors are noisy and its actions do not always have the intended effects. For this reason, the agent language Golog has been extended to models with degrees of belief and stochastic actions. While this allows more precise robot models, the resulting programs are much harder to comprehend, because they need to deal with the noise, e.g., by looping until some desired state has been reached with certainty, and because the resulting action traces consist of a large number of actions cluttered with sensor noise. To alleviate these issues, we propose to use abstraction. We define a high-level and nonstochastic model of the robot and then map the high-level model into the lower-level stochastic model. The resulting programs are much easier to understand, often do not require belief operators or loops, and produce much shorter action traces.

Machine learning has the potential to generate millions of dollars in savings and revenue growth for organizations. But unless ML models are actually put into production, it's just a bunch of useless code. This is the big data science takeaway from New York Life, which recently adopted an MLOps solution from Domino Data Labs to streamline model deployment. Since it was founded in 1845, statistics have played a central role for New York Life. Like all life insurance companies, New York Life dedicates resources to maintaining accurate actuarial tables, which play a big role in determining premiums, payouts, and profits.

Current motion planners, such as the ones available in ROS MoveIt, can solve difficult motion planning problems. However, these planners are not practical in unstructured, rapidly-changing environments. First, they assume that the environment is well-known, and static during planning and execution. Second, they do not support temporal constraints, which are often important for synchronization between a robot and other actors. Third, because many popular planners generate completely new trajectories for each planning problem, they do not allow for representing persistent control policy information associated with a trajectory across planning problems.

Planning in an on-line robotics context has the specific requirement of a short planning duration. A property of typical contemporary scenarios is that (mobile) robots perform similar or even repeating tasks during operation. With these robot domains in mind, we propose database-driven macroplanning for STRIPS (DBMP/S) that learns macros – action sequences that frequently appear in plans – from experience for PDDL-based planners. Planning duration is improved over time by off-line processing of seed plans using a scalable database. The approach is indifferent about the specific planner by representing the resulting macros again as actions with preconditions and effects determined based on the actions contained in the macro. For some domains we have used separate planners for learning and execution exploiting their respective strengths. Initial results based on some IPC domains and a logistic robot scenario show significantly improved (over non-macro planners) or slightly better and comparable (to existing macro planners) performance.

Oliver Hofmann and his research group at the Institute of Solid State Physics at TU Graz are working on the optimization of modern electronics. A key role in their research is played by interface properties of hybrid materials consisting of organic and inorganic components, which are used, for example, in OLED displays or organic solar cells. The team simulates these interface properties with machine-learning-based methods. The results are used in the development of new materials to improve the efficiency of electronic components. The researchers have now taken up the phenomenon of long-range charge transfer.

It takes minutes for most new Minecraft players to work out how to dig up the diamonds that are key to the game, but training artificial intelligence to do it has proved harder than expected. Over the summer, Minecraft publisher Microsoft and other organisations challenged coders to create AI agents that could find the coveted gems. Most can crack it in their first session. But out of more than 660 entries submitted, not one was up to the task. The results of the MineRL - which is pronounced mineral - competition are due to be announced formally on Saturday at the NeurIPS AI conference in Vancouver, Canada.