Public articles linked to the same research event.
arXiv 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.
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.