Action Map Policy: Learning 3D Closed-loop Manipulation via Pixel Classification

Jul 14, 2026

Researchers introduce Action Map Policy (AMP), a method that formulates 3D closed-loop manipulation policy learning as a classification problem in image space. By projecting 3D actions onto camera image planes and treating each pixel as a discrete class, AMP enables millimeter-level precision without requiring a large action vocabulary. Experiments demonstrate that AMP achieves higher success rates and faster inference than strong baseline methods across various manipulation tasks.

Why it matters: This approach offers a novel and efficient action representation for robot learning, potentially enabling more precise and scalable manipulation policies.

Full story at: arXiv Robotics ↗