GORDON learns dense RL rewards from unlabeled video by embedding object-centric scene graphs, and uses the reward's temporal profile to automatically split long-horizon manipulation tasks into subtasks.
Policy invariance under reward transformations: Theory and application to reward shaping,
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GORDON: Graph-based Object-centric Rewards for Decomposition of Long-Horizon Manipulation
GORDON learns dense RL rewards from unlabeled video by embedding object-centric scene graphs, and uses the reward's temporal profile to automatically split long-horizon manipulation tasks into subtasks.