Unified Motion-Action Modeling for Heterogeneous Robot Learning

• URL: http://arxiv.org/abs/2606.16917v3
• Date: Sat, 20 Jun 2026 01:31:21 GMT
• Title: Unified Motion-Action Modeling for Heterogeneous Robot Learning
• Abstract summary: UMA treats object motion and robot actions as co-evolving variables under a masked generative objective.<n>UMA enables multi-task pretraining across heterogeneous data sources without requiring manually annotated task instructions.<n>UMA consistently outperforms state-of-the-art baselines specialized for each inference mode.
• Score: 35.20580699433655
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present Unified Motion-Action (UMA) Model, an approach that uses 3D object motion trajectories as a shared interface to bridge visuomotor control and dynamics modeling. UMA treats object motion and robot actions as co-evolving variables under a masked generative objective, in which the mask pattern determines both the supervision regime during pretraining and the inference mode at deployment. Using hindsight-relabeled motion contexts and a contrastive objective that disentangles task intent from scene geometry, UMA enables multi-task pretraining across heterogeneous data sources without requiring manually annotated task instructions. At deployment, the same pretrained parameters support motion-conditioned visuomotor control, motion-based dynamics modeling, and task adaptation from few-shot demonstrations. Pretrained on a mixture of robot demonstrations, human videos, and simulated data, UMA consistently outperforms state-of-the-art baselines specialized for each inference mode.

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