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A new structured representation for manipulation tasks reveals critical labeling anomalies that traditional methods miss, enhancing both readability and verification.
Rapidly prototype and benchmark robotic navigation scenarios using simple YAML configurations, eliminating the coding barrier in simulation.
Robots that learn from their mistakes *while* navigating? SERP unlocks this by evolving the action model in-context during replanning, boosting success rates and cutting token costs.
