Robots That Adapt: Navigating Uncertainty in Dynamic Environments
![Trajectory optimization under uncertainty-specifically with a half-width of 0.05m and variations in wall position between -0.7m and -0.3m, coupled with a restitution coefficient ranging from 0.7 to 0.9-was subjected to rigorous testing across 200 randomly sampled points within this parameter space, demonstrating the robustness of a five-branched SURE approach to nominal conditions of [latex]x_{\mathrm{wall}} = -0.5\,\mathrm{m}[/latex] and a restitution coefficient of 0.8.](https://arxiv.org/html/2602.06864v1/figures/robustness_comp_cond4.png)
A new framework enables robots to reliably interact with unpredictable environments by intelligently branching and merging potential trajectories.
Science
Beyond the Hype: AI’s Role in Legal Fact-Finding
This review examines how generative AI is changing legal fact verification, focusing on the crucial balance between automation and maintaining professional expertise.
The Responsive Stage: AI-Powered Architecture Comes to Life
A new system allows artists and designers to choreograph dynamic architectural spaces, treating buildings themselves as interactive performers.
Beyond Black Boxes: Understanding AI’s Decision-Making
As artificial intelligence systems grow more complex, simply knowing what they decide isn’t enough – we need to understand why, and current explainability methods fall short when applied to increasingly autonomous agents.
Bridging Reality and Virtual Worlds with Robotic Precision
Researchers have developed a robotic system that physically replicates human movements, offering a standardized way to evaluate the performance of Augmented Reality applications.
Let Data Guide Discovery: A New Framework for Scientific Insight
Researchers have developed a system that autonomously learns and refines scientific principles by actively seeking out and resolving uncertainty in experimental data.
The Writer in the Machine: How Screenwriters Are Navigating AI Collaboration
As generative AI tools become increasingly sophisticated, professional screenwriters are actively reshaping their creative processes to maintain agency and intentionality.
Beyond Scale: Teaching Machines to Understand Physics
![Planetary motion prediction, framed as next-token prediction, reveals a compelling relationship between transformer context length and the learned world model: extended contexts favor global, geometry-based Keplerian models, while limited contexts yield local, force-based Newtonian approximations, demonstrating how inductive biases address inherent failure modes in learning these physical systems-a trade-off elegantly captured by [latex] \text{context length} [/latex].](https://arxiv.org/html/2602.06923v1/x1.png)
New research reveals that simply increasing the size of AI models isn’t enough to instill an understanding of basic physical principles like Newtonian mechanics.
Just Tell It What You Need: Voice Control for Advanced Robotic Wheelchairs
Researchers are exploring how natural language interfaces can simplify control of integrated wheelchair and robotic arm systems, offering a more intuitive experience for users.
Testing the Limits of AI Scientists
![The [latex]
iceTable[/latex] environment from the [latex]
iceMatrix[/latex] package enables clear table presentation by eschewing vertical separators and employing judicious bolding via the [latex]m[/latex] and [latex]
m[/latex] commands, alongside customizable cell backgrounds.](https://arxiv.org/html/2602.06855v1/x2.png)
A new benchmark suite challenges artificial intelligence to perform core tasks within the scientific research process.