AI Control

The field studies both how AI controls robots and other physical systems and how people can keep autonomous AI agents within defined limits. For embodied control, common approaches include vision-language-action policies, world-action models, model-based planning, and closed-loop skill execution, evaluated in suites such as LIBERO and Meta-World as well as on real robots. For agent oversight, researchers use runtime permission gates, shutdown and override mechanisms, and monitors for tool misuse or sabotage, tested in software environments such as BashArena and LinuxArena. A central tension is that agents need to adapt and act over long horizons, while safeguards can miss strategic or unfamiliar failures, making realistic evaluation and persistent intervention important open challenges.