The gap between seeing and doing
A vision-language model can identify an object in a photo. A robot must locate it in three-dimensional space, plan a safe motion, grip it without damage and adapt when the environment changes.
That gap explains why progress in software demos does not instantly translate into universal household robots.
Learning across tasks
Researchers are exploring policies that transfer skills between different actions and settings. Simulation, teleoperation and large collections of movement data can help, but real-world contact remains difficult to model.
Safety constraints and mechanical design are as important as the model that proposes a movement.
The first useful settings
Structured environments such as warehouses and factories offer repeatable tasks and measurable economics. Adoption in homes and public spaces brings much greater variation and social complexity.
The next robotics breakthrough may look ordinary: a machine that completes one useful job reliably every day.