Policies for Active Learning from Demonstration

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Human-in-the-Loop Imitation Learning using Remote Teleoperation (2020, background): “Prior work has attempted to reduce the number of human annotations needed [10, 23, 30] but relabeling is still required.”
Learning from Interventions: Human-robot interaction as both explicit and implicit feedback (2020, background): “This decision can be based on a threshold of actionclassifier confidence [15, 16, 17], distributional distance or discrepancy [18, 19], query by committee [20], or a combination of state novelty and historical error [21].”