The Optimizer Becomes a Policy: Inside RP-1's Learned Planner for Robot World Models
Pantheon Industries introduces Reinforced Planning (RP-1), the first fully learned planner that iteratively improves robot action plans from scratch using Reinforcement Learning over a frozen World Model. RP-1 reimagines planning as a learned policy: starting with a candidate action sequence, it uses a frozen World Model to imagine outcomes, a learned critic to evaluate closeness to goal (value-based scoring instead of latent distance), and a neural planner to revise the sequence over multiple iterations. This replaces hand-designed search heuristics (CEM, MPPI, Adam) with learned, reusable…
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