Robotics

Autonomous systems—from self-driving cars to aerial drones—are bound by one inescapable demand: safety. But encoding safety directly into algorithms is harder than it sounds. We can write explicit constraints (“don’t crash,” “stay upright”), yet the boundary between safe and unsafe states often defies simple equations. The recent paper Learning Neural Control Barrier Functions from Expert Demonstrations using Inverse Constraint Learning (Yang & Sibai, 2025) offers a different path. It suggests that machines can learn what safety looks like—not from rigid formulas, but from watching experts. ...

Embodied Intelligence: A Different Kind of Smart Artificial intelligence is no longer confined to static models that churn numbers in isolation. A powerful shift is underway—toward embodied AI, where intelligence is physically situated in the world. Unlike stateless AI models that treat the world as a dataset, embodied AI experiences the environment through sensors and acts through physical or simulated bodies. This concept, championed by early thinkers like Rolf Pfeifer and Fumiya Iida (2004), emphasizes that true intelligence arises from an agent’s interactions with its surroundings—not just abstract reasoning. Later surveys, such as Duan et al. (2022), further detail how modern embodied AI systems blend simulation, perception, action, and learning in environments that change dynamically. ...