Agent Priors-guided Policy Learning
APPL uses each skill policy's structural prior so robots compose skills and generalize from few demonstrations.
Agent Priors-guided Policy Learning (APPL) treats each policy's structural prior as the interface between skill composition and the skill. A construction agent segments demonstrations, proposes priors, and trains one verified policy per prior, while a runtime agent selects and composes them. Across MetaWorld and long-horizon ManiSkill tasks, APPL improves out-of-distribution skill generalization and enables previously unseen compositions. Ablating the interface information substantially reduces performance.
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