Abstract-- Semantic SLAM (Simultaneous Localization and Mapping) systems enrich robot maps with structural and semantic information, enabling robots to operate more effectively in complex environments. However, these systems struggle in real-world scenarios with occlusions, incomplete data, or ambiguous geometries, as they cannot fully leverage the higher-level spatial and semantic knowledge humans naturally apply. We introduce HICS-SLAM, a Human-in-the-Loop semantic SLAM framework that uses a shared extended reality environment for real-time collaboration. The system allows human operators to directly interact with and visualize the robot's 3D scene graph, and add high-level semantic concepts (e.g., rooms or structural entities) into the mapping process. We propose a graph-based semantic fusion methodology that integrates these human interventions with robot perception, enabling scalable collaboration for enhanced situational awareness. Experimental evaluations on real-world construction site datasets demonstrate improvements in room detection accuracy, map precision, and semantic completeness compared to automated baselines, demonstrating both the effectiveness of the approach and its potential for future extensions.

As collaborative robot (Cobot) adoption in many sectors grows, so does the interest in integrating digital twins in human-robot collaboration (HRC). Virtual representations of physical systems (PT) and assets, known as digital twins, can revolutionize human-robot collaboration by enabling real-time simulation, monitoring, and control. In this article, we present a review of the state-of-the-art and our perspective on the future of digital twins (DT) in human-robot collaboration. We argue that DT will be crucial in increasing the efficiency and effectiveness of these systems by presenting compelling evidence and a concise vision of the future of DT in human-robot collaboration, as well as insights into the possible advantages and challenges associated with their integration.

Let's face it: making decisions is hard. Naturally, with decisions come mistakes, and mistakes are both costly and painful. "It's good to learn from your mistakes. It's better to learn from other people's mistakes." Failing in real life is expensive, but failing in the virtual world is cheap.

A distinct correlation exists between an organization's digital maturity and use of artificial intelligence (AI), a Cognizant report found. Those lower on the digital maturity curve, who classified themselves as beginners, said they were far less likely to consider themselves advanced in AI. While, digital mature organizations--also referred to as leaders--currently invest in AI to generate insights from data, rather than just simply collecting the information. Cognizant's Investing in AI: Moving Along the Digital Maturity Curve, released on Monday, surveyed nearly 2,500 executives globally to determine what it takes to reach digital maturity, identifying AI as the key component. SEE: Artificial intelligence: A business leader's guide (free PDF) (TechRepublic) Nearly 70% of respondents cited themselves as leaders in digital strategy, with the top tactics including implementing AI solutions (38%), replacing legacy systems (60%), and analyzing customer needs (47%), the report found.

A central vision of the Internet of Things is the representation of the physical world in a consistent virtual environment. Especially in the context of smart factories the connection of the different, heterogeneous production modules through a digital shop floor promises faster conversion rates, data-driven maintenance or automated machine configurations for use cases, which have not been known at design time. Nevertheless, these scenarios demand IoT representations of all participating machines and components, which requires high installation efforts and hardware adjustments. We propose an incremental process for bringing the shop floor closer to the IoT vision. Currently the majority of systems, components or parts are not yet connected with the internet and might not even provide the possibility to be technically equipped with sensors. However, those could be essential parts for a realistic digital shop floor representation. We, therefore, propose Virtual Representations, which are capable of independently calculating a physical object's condition by dynamically collecting and interpreting already available data through RESTful Web APIs. The internal logic of such Virtual Representations are further adjustable at runtime, since changes to its respective physical object, its environment or updates to the resource itself should not cause any downtime.

Explosions from a decades-old conflict have left a pockmarked and unstable territory, though many more improvised bombs lie concealed in its vast reaches. Sunlight splays off the beaten edges of Optimus, the smaller robot. If Optimus were programmed to hope, it would hope the object was just a rock and not another bomb. It couldn't afford to take many more hits, and its algorithms have grown wary of the risk. A hulking shape shimmers in the heat as it approaches Optimus, lolling like a huge, headless cowboy.

Once large law firms had armies of first-year law graduates, combing documents for relevant information; now machines largely do it. New artificial intelligence diagnosed lung cancer 50 percent more accurately than radiology experts last year. And the U.S. Postal Service plans to deploy autonomous trucks by 2025. These are signs of big change, precipitated by a wave of new artificial intelligence resulting from a perfect storm of investments and development these past five years. And coming developments will increasingly enable machines to do more mental and physical tasks faster, better and cheaper than humans.