GiB uses self-supervised latent features and Mahalanobis distance to filter erroneous subtasks from mixed-quality human demonstrations, improving robot policy learning in simulation and real-world tasks.
Imitation learning: A survey of learning methods
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Good in Bad (GiB): Sifting Through End-user Demonstrations for Learning a Better Policy
GiB uses self-supervised latent features and Mahalanobis distance to filter erroneous subtasks from mixed-quality human demonstrations, improving robot policy learning in simulation and real-world tasks.