Language-Conditioned Robot Manipulation

The field studies how robots can interpret instructions such as which object to move and how, then carry them out through visual perception and motor control. Common approaches include vision-language-action policies trained on demonstrations, language-model planners paired with reusable skills, and methods that add geometric grounding, affordances, or predictive world models. Researchers evaluate these systems in simulation benchmarks such as LIBERO and RoboTwin as well as on physical robots, including long-horizon and cluttered tasks. A central challenge is ensuring that success reflects genuine instruction grounding and transfers reliably to new objects, scenes, and robot bodies while remaining safe under uncertainty.