Interactive Robot Learning – Stanford ILIAD

Stanford Intelligent and Interactive Autonomous Systems Group

Data Quality in Imitation Learning HYDRA improves the sample efficiency for learning real world long-horizon tasks by modifying robot action spaces to reduce online distribution shift. In robotics, scaling up data collection for imitation learning is often challenging, and our test settings are constantly shifting from the training data. Therefore, if we want robots to learn new tasks scalably, we need to be more sample efficient with both learning methods and the type of data we collect. We study the interaction between models and data sources for learning manipulation tasks in robotics,…

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