Robotics

Opening — Why this matters now Robotics has quietly entered an awkward phase. Models can see remarkably well and talk impressively about tasks—but when it comes to executing long-horizon, high-precision actions in the physical world, performance still collapses in the details. Grasp slips. Motions jitter. Multimodal uncertainty wins. At the same time, video generation models have undergone a renaissance. Large diffusion-based video models now encode temporal causality, implicit physics, and motion continuity at a scale robotics has never had access to. The obvious question follows: ...

Opening — Why this matters now Embodied AI has a dirty secret: most simulated worlds look plausible until a robot actually tries to use them. Chairs block drawers, doors open into walls, and walkable space exists only in theory. As robotics shifts from toy benchmarks to household-scale deployment, this gap between visual realism and functional realism has become the real bottleneck. ...

Opening — Why this matters now Robotics has reached an awkward adolescence. Vision–Language–Action (VLA) models can now describe the world eloquently, name objects with near-human fluency, and even explain why a task should be done a certain way—right before dropping the object, missing the grasp, or confidently picking up the wrong thing. This is not a data problem. It’s a diagnostic one. ...

Opening — Why this matters now Humanoid robots can now run, jump, and occasionally impress investors. What they still struggle with is something more mundane: noticing the stairs before falling down them. For years, reinforcement learning (RL) has delivered impressive locomotion demos—mostly on flat floors. The uncomfortable truth is that many of these robots are, functionally speaking, blind. They walk well only because the ground behaves politely. Once the terrain becomes uneven, discontinuous, or adversarial, performance collapses. ...

Opening — Why this matters now Large Language Models are very good at knowing. They are considerably worse at helping. As AI systems move from chat interfaces into robots, copilots, and assistive agents, collaboration becomes unavoidable. And collaboration exposes a deeply human cognitive failure that LLMs inherit wholesale: the curse of knowledge. When one agent knows more than another, it tends to communicate as if that knowledge were shared. ...

Opening — Why this matters now Robotic manipulation has always had a split personality. Vision plans elegantly in slow motion; force reacts brutally in real time. Most learning systems pretend this tension doesn’t exist — or worse, paper over it with handcrafted hierarchies. The result is robots that see the world clearly but still fumble the moment contact happens. ...

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. ...