A new collaborative robotic system is expanding assistive technology beyond simple navigation, empowering visually impaired individuals with greater independence in complex environments.
New research demonstrates that artificial intelligence can meaningfully accelerate scientific discovery when combined with robust validation procedures.
A novel research paradigm integrating artificial intelligence and materials science is emerging, promising to accelerate the design and discovery of advanced materials.
![The system architecture establishes a framework for [latex] n [/latex] interconnected modules, enabling scalable and modular implementation of complex functionalities.](https://arxiv.org/html/2603.13126v1/figure1.png)
Researchers have created a cloud-based platform, PsyCogMetrics™ AI Lab, designed to move beyond simple performance scores and rigorously evaluate the cognitive capabilities of large language models.
A new framework intelligently schedules perception modules, focusing computational resources on the most relevant information to enhance efficiency and reduce latency in collaborative robotics.
A new approach fine-tunes pre-trained navigation policies using reinforcement learning, allowing robots to safely and reliably explore environments they’ve never seen before.
New research demonstrates that learning to predict future states within a latent space yields superior representations of physical systems compared to traditional reconstruction or autoregressive methods.
New research shows robots can strategically sacrifice their own parts to adapt to challenging terrains and improve locomotion performance.
![Time series decomposition enables the creation of an extended model [latex]M^\hat{M}[/latex] by transforming an original time series [latex]\mathbf{x}[/latex] into component representations [latex]{\mathbf{C}\_{\mathbf{x}}}[/latex] via a forward function FF, and then leveraging the inverse transformation [latex]F^{-1}(\cdot)[/latex] in combination with the original time series network MM, all without requiring model retraining.](https://arxiv.org/html/2603.12880v1/x1.png)
New research explores how to build artificial intelligence systems that not only predict health metrics from wearable sensors, but also clearly explain why they made those predictions.
Researchers have developed a new approach to controlling deformable objects – like cables or hoses – alongside rigid structures, enabling more complex manipulation in cluttered environments.