Causal Object-Centric Models for Planning with Monte Carlo Tree Search

In three linesCOMET combines a frozen unsupervised object-centric encoder with a transformer-based world model to perform Monte Carlo Tree Search in slot-structured latent space. A novel action-slot fusion mechanism binds actions to objects. Evaluated on 8 tasks (Object-Centric Visual RL, ManiSkill, Robosuite, VizDoom), COMET outperforms object-centric and monolithic baselines in early training stages.

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