FIL pools imitation-learning policies from robots with different sensors, pseudo-labels cloud data with the median of robot outputs, and uses the resulting guide model for transfer learning.
Towards robust skill generalization: Unifying learning from demonstration and motion planning,
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Federated Imitation Learning: A Privacy Considered Imitation Learning Framework for Cloud Robotic Systems with Heterogeneous Sensor Data
FIL pools imitation-learning policies from robots with different sensors, pseudo-labels cloud data with the median of robot outputs, and uses the resulting guide model for transfer learning.