# Agentic Manipulation Policies

Robot manipulation has relied on specialist policies trained on demonstrations, such as vision-language-action models (VLAs) and world-action models (WAMs). AMP1 keeps a generalist AI model in the control loop and has it command arm motion through code, reaching 85.6% zero-shot success on RoboLab-120 against 49.2% and 42.9% for leading VLA and WAM policies. It succeeded in 104 of 108 real-world trials across twelve tasks and three robot instances, with no demonstrations or task-specific training.

Robot manipulation has relied on specialist policies trained on demonstrations, such as vision-language-action models (VLAs) and world-action models (WAMs). AMP1 keeps a generalist AI model in the control loop and has it command arm motion through code, reaching 85.6% zero-shot success on RoboLab-120 against 49.2% and 42.9% for leading VLA and WAM policies. It succeeded in 104 of 108 real-world trials across twelve tasks and three robot instances, with no demonstrations or task-specific training.