Cosmos3

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1. Oct 19
   EVO-WAM lets world action models self-train on verified imagined trajectories

   Researchers behind EVO-WAM report a self-training loop in which a world action model generates complete imagined video-action trajectories, a vision-language model checks whether the task was completed, and an inverse dynamics model checks that the generated actions match the imagery before verified segments are used for fine-tuning. The team reports that Cosmos3's average success rate rose from 26.9% to 68.0% on seven unseen RoboTwin 2.0 simulation tasks and from 20.0% to 76.7% on three unseen long-horizon real-robot tasks with 10 trials each, while DreamZero improved from 28.5% to 46.4%. An ablation found 43.7% fourth-round success with vision-only filtering versus 68.0% with the action-consistency check, and the authors note gains were not monotonic, with a dip in round three.

   36氪 人工智能 · 🔥 0

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