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By recasting the Hamilton-Jacobi-Bellman equation as a tractable Monte Carlo estimation, this work stabilizes physics-informed RL and unlocks its potential for high-dimensional control tasks.
LLMs can now plan robot manipulation of clutter for navigation in previously inaccessible environments, enabling zero-shot generalization in interactive object placement tasks.
