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Abstract Ergodic search enables optimal exploration of an information distribution while guaranteeing the asymptotic coverage of the search space. However, current methods typically have exponential computation complexity in the search space dimension and are restricted to Euclidean space.

Update Recently, Katie Hughes, a master of science in robotics student at Northwestern University, deployed our social navigation algorithm on an untethered Unitree Go1 quadruped and tested it in a densely packed atrium.