DynaMo: In-Domain Dynamics Pretraining for Visuo-Motor Control
Classification
View four quadrants- Major category
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- Quadrant
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- Prediction paradigm
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- Subcategories
- Latent-action & representation methods
- Source review status
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Category review. Action-free inverse/forward dynamics pretrain visual features used by separate visuomotor policies. This is relevant representation-method context for WAMs, rather than a canonical pretrained backbone or a deployed future-prediction controller. The paper is retained for its adjacent resource/method role; WAM joint-prediction/IDM architecture quadrants do not apply to the cataloged contribution. Reading evidence
Contribution
DynaMo uses action-free dynamics pretraining to make visual features useful for imitation learning. An encoder, latent inverse model and forward model jointly learn to predict next-frame embeddings; a separate policy then learns from frozen features and labeled demonstrations. Results favor DynaMo on several manipulation tasks, but include ties and initialization regressions. The contribution is a representation-learning objective, with no demonstrated use of the dynamics models for online planning.
Abstract
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Affiliations
New York University