New research demonstrates that standard reinforcement learning techniques, when carefully tuned, can reliably transfer policies learned in simulation to physical robots for stable and efficient online learning.
A new systematic review explores the emerging concept of ‘agentic skills’ and how reusable procedural knowledge is key to unlocking the next generation of powerful and reliable AI agents.
![The study demonstrates that the interaction interval within a robotic swarm is governed by a relationship between the number of robots ([latex] NN [/latex]), communication range ([latex] CC [/latex]), and the dimensions of the operational environment ([latex] LL [/latex]), all considered in relation to the robots’ velocity ([latex] vv [/latex]), as evidenced by reported means and inter-quartile ranges.](https://arxiv.org/html/2602.21148v1/x4.png)
New research details how information propagates through robot swarms relying solely on direct, opportunistic communication between individuals.
![A hierarchical framework for analyzing parameter identifiability integrates eigenvalue decomposition and the Schur complement to categorize parameters across scales, revealing that predictive uncertainty stems from non-identifiable subspaces-specifically, contributions from zero-order non-identifiable parameters [latex]\boldsymbol{U\_{k-r\_{0}}^{\to p}\theta}[/latex] and first-order non-identifiable parameters [latex]\boldsymbol{U\_{k-r\_{0}-r\_{1}}^{\to p}\theta}[/latex]-and quantifying this uncertainty through a metric [latex]\mathcal{K}\_{i}[/latex] that defines practical identifiability.](https://arxiv.org/html/2602.20495v1/x1.png)
A new computational framework reveals how accurately we can estimate parameters in complex biological models, paving the way for more reliable data-driven discoveries.
A new reinforcement learning framework guides vision-language models to master complex, long-duration manipulation tasks through interactive feedback.
A new study examines how collaborative cybersecurity teams-pairing human experts with artificial intelligence-perform in competitive hacking challenges.
Researchers are exploring how modeling the internal monologue humans use to guide actions can dramatically improve the realism, diversity, and controllability of artificial intelligence agents.
New research reveals key distinctions in how transformer-based models process proteins versus human language, impacting model efficiency and performance.
A new robotic platform dramatically accelerates the development of resilient soft actuators, paving the way for more robust and adaptable robotic systems.
A new approach to software design prioritizes formal verification and runtime safety for the next generation of AI-powered applications.