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Direct-decision models exhibit a learned "ordinal-scale-utilization bias," compressing the effective decision space and underutilizing available scale points, even when accuracy is high.
Achieving up to 97% on-time arrival in cluttered environments, LP-NavOA redefines humanoid robot navigation by seamlessly integrating local planning and obstacle avoidance without global mapping.