GeoLAM: Learning Geometry-Grounded Latent Actions from Unlabeled Human Videos

• URL: http://arxiv.org/abs/2609.17099v1
• Date: Tue, 15 Sep 2026 12:31:34 GMT
• Title: GeoLAM: Learning Geometry-Grounded Latent Actions from Unlabeled Human Videos
• Abstract summary: We present GeoLAM, a framework for learning geometry-grounded latent actions from action-free human videos.<n>GeoLAM combines future-frame reconstruction through a frozen geometric feature hierarchy with motion supervision from a training-only 4D geometry teacher.
• Score: 8.414561051327915
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human videos provide rich manipulation experience, but extracting action representations that preserve useful motion remains challenging. Visual reconstruction alone can entangle manipulation-related motion with appearance changes and camera movement. We present GeoLAM, a framework for learning geometry-grounded latent actions from action-free human videos. GeoLAM combines future-frame reconstruction through a frozen geometric feature hierarchy with motion supervision from a training-only 4D geometry teacher. The geometric representation provides a structural prior, while the teacher's predictions yield spatially pooled targets capturing 3D displacement, residual image-plane motion, and surface-orientation changes. Visibility and confidence weighting reduces the contribution of unreliable estimates, encouraging continuous latent actions to retain geometric motion without explicit hand-pose or hand-trajectory annotations. After video pretraining without action labels, the learned representation provides transition targets for a world-action model trained on action-labeled robot demonstrations. The model jointly denoises latent actions and executable action chunks, with future-video prediction used only as an auxiliary training task. Deployment therefore requires neither the geometry teacher nor future-video generation. Evaluations on a latent-action benchmark and robotic manipulation tasks demonstrate the strong performance of GeoLAM.

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