Building Intelligence, Block by Block
A new review argues that the brain’s modular design holds the key to creating more flexible and powerful artificial intelligence systems.
Science
Shaping the Future: AI-Powered Control for Soft Robots
![The research extends closed-loop inverse kinematics (CLIK) to infinite dimensions, enabling task-solving reasoning across the entirety of a soft robot’s shape, and overcomes the practical difficulty of obtaining analytical Jacobians for such models through the implementation of a neural network embedding learned from simulations [latex] \mathbb{J} [/latex].](https://arxiv.org/html/2602.18655v1/fig/abstract-no-background.png)
Researchers are leveraging neural networks to achieve precise, closed-loop control of soft robots, unlocking new possibilities for complex manipulation and locomotion.
Beyond the Story: Rigorous Evaluation for AI Understanding
A new framework moves mechanistic interpretability research beyond qualitative narratives, demanding verifiable results through executable code.
Learning to Learn: The Next Step for Imitation
A new perspective on imitation learning focuses on building agents that can adapt to unseen situations, moving beyond simply copying demonstrated behavior.
From Research to Routine: How AI is Changing Scientific Workflows
A new study explores how large language models can automate content creation in science, shifting the focus to oversight and quality assurance.
Beyond Prediction: How Combining Foresight and Communication Improves Human-Robot Collaboration
![User experience assessment across three experimental rounds demonstrated that while initial attempts to predict user intent via force and velocity showed limited improvement over a baseline, integrating voice command recognition consistently yielded statistically significant gains in perceived ease of use [latex] (p<0.05, p<0.01, p<0.001) [/latex], suggesting a practical pathway toward more intuitive human-robot interaction despite the inherent challenges of predicting complex user behavior.](https://arxiv.org/html/2602.18850v1/Fig4.png)
New research suggests that the most effective partnerships between humans and robots aren’t built on flawlessly anticipating our needs, but on a blend of prediction and clear, direct communication.
Sensing the Future: AI Powers the Next Generation of Materials Analysis
Artificial intelligence and machine learning are transforming surface plasmon resonance and spectroscopy, enabling faster, more accurate materials characterization and driving the development of self-driving laboratories.
Robots in Public: A Practical Guide for Researchers
Deploying robots in real-world settings presents unique challenges, and this article offers a collaboratively-built resource to help navigate them.
Beyond Automation: Reimagining Science Education with AI
A new review calls for a thoughtful, human-centered approach to integrating artificial intelligence into science classrooms, prioritizing ethical considerations and equitable access.
Robots That Feel: Understanding Hardness Through Touch and Sight
Researchers have developed a new framework enabling robots to assess object hardness – like determining the ripeness of fruit – and articulate their reasoning in human-understandable language.