Teaching Robots using Interactive Imitation Learning

As robots transition from controlled environments, such as industrial settings, to more dynamic and unpredictable real-world applications, the need for adaptable and robust learning methods becomes paramount. In this dissertation we develop Interactive Imitation Learning (IIL) based methods that allow robots to learn from imperfect demonstrations. We achieve this by incorporating human factors such as the quality of their demonstrations and the level of effort they are willing to invest in teaching the robot.

Our research is structured around three key contributions. First, we examine scenarios where robots have access to high-quality human demonstrations and abundant corrective feedback. In this setup, we introduce an algorithm called SARI (Shared Autonomy across Repeated Interactions), that leverages repeated human-robot interactions to learn from humans. Through extensive simulations and real-world experiments, we demonstrate that SARI significantly enhances the robot's ability to perform complex tasks by iteratively improving its understanding and responses based on human feedback.

Second, we explore scenarios where human demonstrations are suboptimal and no additional corrective feedback is provided. This approach acknowledges the inherent imperfections in human teaching and aims to develop robots that can learn effectively under such conditions. We accomplish this by allowing the robot to adopt a risk-averse strategy that underestimates the human's abilities. This method is particularly valuable in household environments where users may not have the expertise or patience to provide perfect demonstrations.

Finally, we address the challenge of learning from a single video demonstration. This is particularly relevant for enabling robots to learn tasks without extensive human involvement. We present VIEW (Visual Imitation lEarning with Waypoints), a method that focuses on extracting critical waypoints from video demonstrations. By identifying key positions and movements, VIEW allows robots to efficiently replicate tasks with minimal training data. Our experiments show that VIEW can significantly reduce both the number of trials required and the time needed for the robot to learn new tasks.

The findings from this research highlight the importance of incorporating advanced learning algorithms and interactive methods to enhance the robot's ability to operate autonomously in diverse environments. By addressing the variability in human teaching and leveraging innovative learning strategies, this dissertation contributes to the development of more adaptable, efficient, and user-friendly robotic systems.