💡 The paper proposes a Geometric Action Model for robot policy learning that leverages pretrained geometric foundation models to enable language-conditioned manipulation policies in 3D physical environments. The problem addressed is that current vision-language-action models and video world-action models operate primarily on 2D image frames or 2D-derived latent spaces, leaving implicit the 3D geometry required for contact-rich manipulation.

The proposed method, Geometric Action Model, repurposes a pretrained geometric foundation model as a shared substrate for perception, temporal prediction, and action decoding. It splits the model at an intermediate layer, using the shallow layers as an observation encoder and inserting a causal future predictor to forecast future latent tokens conditioned on language, proprioception, and action history. The predicted future tokens are then routed through the remaining model blocks for feature propagation and decoding, allowing a single backbone to produce both future geometry and actions.

The results show that the Geometric Action Model is more accurate, more robust, faster, and lighter than current foundation-model-scale baselines across a broad suite of simulation and real-robot manipulation benchmarks. This design equips the geometric foundation model with language-conditioned temporal world modeling through minimal architectural modification while preserving its rich geometric priors, making it a significant contribution to robot policy learning in 3D physical environments.