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Found 4 papers, 2 papers with code
Imitation Learning with Human Eye Gaze via Multi-Objective Prediction
In this work, we propose Gaze Regularized Imitation Learning (GRIL), a novel context-aware, imitation learning architecture that learns concurrently from both human demonstrations and eye gaze to solve tasks where visual attention provides important context.
PODNet: A Neural Network for Discovery of Plannable Options
Learning from demonstration has been widely studied in machine learning but becomes challenging when the demonstrated trajectories are unstructured and follow different objectives.
Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Dense and Sparse Reward Environments
However, it is currently unclear how to efficiently update that policy using reinforcement learning as these approaches are inherently optimizing different objective functions.
Efficiently Combining Human Demonstrations and Interventions for Safe Training of Autonomous Systems in Real-Time
This paper investigates how to utilize different forms of human interaction to safely train autonomous systems in real-time by learning from both human demonstrations and interventions.
