Attacca

Headlines · 1

1. Oct 5
   Attacca: goal-directed control for long-horizon embodied agents

   An arXiv paper introduces Attacca, a method for training visual goal-conditioned policies for long-horizon embodied agents. It uses context-decoupled goal sampling to pair each demonstration with a class-compatible masked goal image from another world, adds a target-mask prediction head for dense current-view grounding, and conditions the policy on Search, Approach and Interact phases. On short- and long-horizon Minecraft tasks, the authors report 39.0–47.5% clean success, a 1.7–2.4x gain over the strongest baseline, and 54%, 30% and 28% completion on long-horizon tasks, up to a 7x improvement.

   Hugging Face · Papers · 🔥 0

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