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This work introduces time as a new dimension of interpretability, coined as temporal interpretability, and demonstrates how temporal abstractions via action chunking improve it, by first introducing two novel policy gradient algorithms that incorporate action chunking.
This work separates behavior learning from temporal composition, allowing demonstrated behaviors to be reused in longer, previously unseen temporal compositions without retraining, and demonstrates reliable execution of complex temporal instructions and additional safety constraints, with substantial improvements over...
Reinforcement learning agents trained to maximize their own reward in repeated interactions can converge to supra-competitive outcomes resembling explicit collusion, without communication or shared design. Existing mitigation approaches are largely tied to specific economic settings, like two-sided platforms and auctio...
This paper introduces hint, a method for guiding short-horizon policies toward satisfying complex LTL specifications at inference time using hierarchical world models, and shows that hint$^2$ can handle complex instructions on a real UR5e manipulator.
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