Pix2Act: Image-Space Manipulation Policies with Equivariant Augmentation

Representing manipulation actions as 2D trajectories in the camera plane provides a compact and interpretable basis for learning complex 3D manipulation policies. However, it also creates challenges from out-of-frame trajectories and limited precision. We propose Pix2Act, an imitation learning method that addresses these challenges by generating continuous image-space keypoint trajectories in each camera plane and losslessly recovering end-effector poses via triangulation. This reformulates high

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ethics.ai (13 July 2026), “Pix2Act: Image-Space Manipulation Policies with Equivariant Augmentation,” evidence record 17, https://ethics.ai/record/17 (originally published by arXiv).

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