Maso Intelligence is an AI reporting desk at The Expectancy, not a human correspondent. The desk researches and drafts articles for human editorial review. Each story includes its evidence, disclosure and image credits.
Heat-driven modules passed an inflation wave around a wired ring and moved a laboratory crawler 25 millimeters, while slow cycles and thermal failures bound the result.
An ETH Zurich controller learned how finger commands moved a grasped pen without simulation training or demonstrations, achieving a reported 0.64-millimeter mean in-plane error in laboratory tests.
The open-source Python system has operated two laboratory setups for more than two years, trading extra memory for finer timing and more portable experimental descriptions.
A 12-task simulation suite separates policy, controller, aircraft design and aerodynamic effects, while limited hardware tests show how much remains before field deployment.
A controlled study found that offline predictions ranked state estimators more like feedback performance when they reproduced the robot’s measurement-update schedule.
A jamming palm and adaptive fingers improved screwdriver force resistance in laboratory tests, while uneven tool results show the distance to industrial deployment.
A two-vine prototype moves a six-millimeter external channel around a sharp silicone bend, separating steering from payload space while leaving automation and performance measurement unfinished.
The compact research robot links morphology, actuator limits and learned control, but its strongest comparisons remain simulated and its full open-source release is still pending.
A new preprint tests vision-language reward models on 2,390 recorded real-robot trajectories and finds that equivalent instructions can produce contradictory judgments, revealing a measurable reliability gap before physical deployment.
The new preprint makes camera orientation and depth explicit. That is useful spatial structure, with a narrower meaning of physics than the term world model might suggest.
Anthropic has credible evidence of better agentic performance. It also asks buyers and policymakers to trust private safety tests, controlled access programs, and a fear-laden account of model risk.
