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Category: mapping – Page 4

AI search robot uses 3D maps and internet knowledge to find lost items

A robot that can locate lost items on command, the latest development at the Technical University of Munich (TUM), combines knowledge from the internet with a spatial map of its surroundings to efficiently find the objects being sought. The new robot from Prof. Angela Schoellig’s TUM Learning Systems and Robotics Lab looks like a broomstick on wheels with a camera mounted at the top. It is one of the first robots that not only integrates image understanding but also applies it to a clearly defined task.

To find a pair of glasses misplaced in the kitchen, for example, the robot has to look around and build a three-dimensional image of the room. The camera initially provides two-dimensional images, but these pixels also contain depth information. This creates a spatial map of the environment that is accurate to the centimeter and is constantly updated. A laptop also provides the robot with information about which objects are visible in the image and what significance they have for humans.

“We have taught the robot to understand its surroundings,” says Prof. Schoellig. The head of the Robotics Lab at the TUM Chair of Safety, Performance and Reliability for Learning Systems aims to develop robots that can navigate any environment independently. Humanoid robots working in factories or robots in care settings in private homes require this newly developed basic understanding, which, as Schoellig explains, “is important for all robots that move in spaces that are constantly changing.” A paper introducing the technology is published in the journal IEEE Robotics and Automation Letters.

A foundation model of vision, audition, and language for in-silico neuroscience

‘The present results strengthen the possibility of a paradigm shift in neuroscience… moving from the fragmented mapping of isolated cognitive tasks toward the use of unified, predictive foundation models of brain and cognitive functions By aligning the representations of Al systems to those of the human brain, we demonstrate that a single architecture can integrate a vast range of fMRI responses across hundreds of individuals, extending the framework that led the 2025 Algonauts competition. The observed log-linear scaling of encoding accuracy mirroring power laws in both artificial intelligence and neuroscience suggests that the ceiling for predicting human brain activity is yet to be reached.’

Cognitive neuroscience is fragmented into specialized models, each tailored to specific experimental paradigms, hence preventing a unified model of cognition in the human brain. Here, we introduce TRIBE v2, a tri-modal (video, audio and language) foundation model capable of predicting human brain activity in a variety of naturalistic and experimental conditions. Leveraging a unified dataset of over 1,000 hours of fMRI across 720 subjects, we demonstrate that our model accurately predicts high-resolution brain responses for novel stimuli, tasks and subjects, superseding traditional linear encoding models, delivering several-fold improvements in accuracy. Critically, TRIBE v2 enables in silico experimentation: tested on seminal visual and neuro-linguistic paradigms, it recovers a variety of results established by decades of empirical research.

Building a National Quantum Strategy

Andrea Damascelli has always been fascinated by light. He uses it to probe materials on an atomic level, and his observations have contributed to the condensed-matter community’s understanding of high-temperature superconductors and quantum materials. His research group at the University of British Columbia (UBC) uses time-, spin-, and angle-resolved photoemission spectroscopy, an intricate technique that maps the energy and velocity of electrons as they propagate through materials.

In 2015, Damascelli spearheaded efforts that brought one of the first Canada First Research Excellence Fund (CFREF) grants to UBC’s Quantum Matter Institute. As the institute’s scientific director, he found himself at the helm of a full-blown research center—hiring faculty, expanding staff, and upgrading facilities. A few months later, he received a special request from Canada’s National Research Council: join leaders from across Canada’s quantum ecosystem to advise on a strategy for growing the country’s quantum community as a whole.

Physics Magazine chatted with Damascelli as he looked back on the beginning of Canada’s first National Quantum Strategy (NQS) and looked forward to developing a self-sustaining quantum research and training powerhouse.

Technical Advance alert 🙌

https://doi.org/10.1172/jci.insight.

In this Research article, Benjamin D. Philpot & team establish a multimodal dual-reporter mouse that accelerates AngelmanSyndrome therapeutic development through scalable cell-based screening, high-resolution whole-brain mapping, non-invasive live imaging, and sorting neurons with unsilenced paternal Ube3a.

2Animal Models Core.

3Department of Genetics, and.

4Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

The E3-ome gene-centric compendium reveals the human E3 ligase landscape

Now online! The E3-ome defines the human repertoire of ubiquitin E3 ligases, creating a unified resource that maps their diversity across the ubiquitin and ubiquitin-like systems. By consolidating fragmented knowledge, this framework provides a foundation for studying ubiquitin signaling and accelerating discovery.

Wind-powered robot could enable long-term exploration of hostile environments

Researchers at Cranfield University have created WANDER-bot, a low-cost, 3D-printed robot that is powered by wind energy. Designed to spend long durations in hostile, windy environments such as certain deserts, polar regions or even other planets, WANDER-bot doesn’t need a battery to power movement, enabling longer operations without having to pause and recharge.

Movement accounts for around 20% of battery use in most robots, so running on natural energy makes WANDER-bot an efficient solution for long-term exploration or mapping of unknown terrains. As a result, any electronic elements added to future versions for data collection or transmission purposes could have their own smaller, lighter power source. Using natural energy also counters the issue of performance degradation over time in traditional power sources, such as solar cells and radioisotope thermoelectric generators.

Designed by Dr. Saurabh Upadhyay and Sam Kurian, Research Associate in Space Engineering, the robot uses parts that are entirely 3D printed, with the design deliberately simple to allow for quick repair and replacement. This means that, in theory, you could print and construct WANDER-bot anywhere and make replacement parts in situ as needed, removing the need for time-consuming and costly resupply missions.

Synaptic connectivity alone can reveal neuron types

Recent technological advances facilitate the reconstruction of complete brain connectomes in small organisms and partial connectomes in mammals, involving the mapping of the network of neurons and synaptic connections. Accurate cell typing of these connectomes aids in interpreting circuit functions and comparing brain organization across species.

Traditionally, cell typing relied on manual morphological classification by experts—a slow process that required detailed anatomical information. However, morphology can be deceptive or inadequate in many brain regions, especially in circuits with repeated cell types, where neurons can share very similar morphology despite differing in connectivity.

DESI maps C-19, an extremely metal-poor Milky Way stellar stream

Using the Mayall 4-meter telescope at Kitt Peak National Observatory, an international team of astronomers has observed C-19—an extremely metal-poor stellar stream in the Milky Way’s halo. Results of the observational campaign, published March 11 on the arXiv pre-print server, provide crucial insights into the properties of this stellar stream.

Stellar streams are remnants of dwarf galaxies or globular clusters (GCs) that once orbited a galaxy but have been disrupted and stretched out along their orbits by tidal forces of their hosts. Observations show that many stellar streams are elongated debris of tidally disrupted globular clusters.

Studies of galactic stellar streams could answer some crucial questions about the Milky Way. For instance, they could help us understand the large-scale mass distribution of the galactic dark matter halo. Moreover, the investigation of stellar streams could confirm whether or not our galaxy contains low-mass dark matter subhalos.

New 4D vision chip can help robots track distance and speed at once

Researchers at Pointcloud GmbH in Zürich, Switzerland, have packed advanced 4D sensing technology — once too bulky for everyday use — onto a single silicon chip.

It’s a 4D imaging sensor that maps the physical world while simultaneously clocking the speed of every object it sees. It offers a low-cost, high-speed vision solution for everything from autonomous drones to future smartphones.

“This result demonstrates the capabilities of FMCW LiDAR FPA sensors as enablers of ubiquitous, low-cost, compact coherent 4D imaging cameras,” the researchers wrote in the study paper.

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