Efficiently Combining Human Demonstrations and Interventions for Safe Training of Autonomous Systems in Real-Time
This work addresses safety and efficiency in training autonomous systems, such as drones, but is incremental as it builds on existing frameworks like imitation learning.
The paper tackles the problem of safely training autonomous systems in real-time by combining human demonstrations and interventions, showing that this method improves task completion performance and reduces data requirements by 32% compared to using demonstrations alone.
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. We implement two components of the Cycle-of-Learning for Autonomous Systems, which is our framework for combining multiple modalities of human interaction. The current effort employs human demonstrations to teach a desired behavior via imitation learning, then leverages intervention data to correct for undesired behaviors produced by the imitation learner to teach novel tasks to an autonomous agent safely, after only minutes of training. We demonstrate this method in an autonomous perching task using a quadrotor with continuous roll, pitch, yaw, and throttle commands and imagery captured from a downward-facing camera in a high-fidelity simulated environment. Our method improves task completion performance for the same amount of human interaction when compared to learning from demonstrations alone, while also requiring on average 32% less data to achieve that performance. This provides evidence that combining multiple modes of human interaction can increase both the training speed and overall performance of policies for autonomous systems.