# dexterous robot hand manipulation

DexForesight injects predictive features from an action-conditioned spatial world model into a dexterous policy's latent space through query alignment, and at deployment keeps only the policy, which acts from RGB, language and robot state. On 11 DexJoCo simulation tasks it raises the GR00T N1.7 backbone's overall success from 49.4% to 60.5%, and on three real-robot tasks it reaches 58.3%, 8.3 points above the strongest baseline. The world model, depth encoder and future-prediction heads are all removed at inference.

DexForesight injects predictive features from an action-conditioned spatial world model into a dexterous policy's latent space through query alignment, and at deployment keeps only the policy, which acts from RGB, language and robot state. On 11 DexJoCo simulation tasks it raises the GR00T N1.7 backbone's overall success from 49.4% to 60.5%, and on three real-robot tasks it reaches 58.3%, 8.3 points above the strongest baseline. The world model, depth encoder and future-prediction heads are all removed at inference.

GOTT introduces a reach-acquire-move framework in which a single cross-embodiment closed-loop contact-acquisition primitive turns a robot-agnostic object trajectory and reach specification into stable contact, after which a pose-conditioned controller tracks the desired object motion; in simulation the unified policy reaches 78.7% average grasping success versus 51.7% for CrossDex, transfers zero-shot to unseen Allegro variants at 72.1% versus 17.2% for an Allegro-only policy, and achieves 80.0% average task success on four real-world tasks versus 35.0% for open-loop baselines.

GOTT introduces a reach-acquire-move framework in which a single cross-embodiment closed-loop contact-acquisition primitive turns a robot-agnostic object trajectory and reach specification into stable contact, after which a pose-conditioned controller tracks the desired object motion; in simulation the unified policy reaches 78.7% average grasping success versus 51.7% for CrossDex, transfers zero-shot to unseen Allegro variants at 72.1% versus 17.2% for an Allegro-only policy, and achieves 80.0% average task success on four real-world tasks versus 35.0% for open-loop baselines.