Attacca: Goal-Directed Control under State Continuity for Long-Horizon Embodied Agents

The researchers propose Attacca, which trains a vision goal-conditioned policy on complete search-to-interaction trajectories so long-horizon embodied agents can carry on to the next task from the position, orientation and world state left by the previous one. The method uses context-decoupled goal sampling, pairing each demonstration with a class-compatible masked goal image from another world, and provides supervision beyond action imitation through a goal mask prediction head, while introducing behavior phase conditioning that distinguishes the Search, Approach and Interact phases.