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About this paper
Michal Korniak, Marco Bagatella, Ralf Römer, Michal Nauman, Yarden As, Pranav Vignesh, Manan Agarwal, Leander Diaz-Bone and 16 more
The authors evaluate critic-guided reinforcement learning for improving a generalist robot policy across simulated manipulation tasks. The methods learn from experience across varied tasks and use action-value estimates to guide action selection or policy updates. In MolmoSpaces, gains reach 58 percentage points on unseen scenes and 39 on unseen objects, but are maxima in tested settings, not arbitrary-task generalization.
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