Heterogeneous Learning from Demonstration
This work addresses the challenge of improving human-robot collaboration by enhancing robot autonomy, though it appears incremental as it builds on existing learning from demonstration methods with a specific Bayesian approach.
The paper tackles the problem of enabling robots to infer human needs by detecting and classifying partner heterogeneity, proposing a Bayesian inference framework for learning from heterogeneous demonstration and achieving up to 12.8% improvement over conventional methods on a StarCraft II dataset.
The development of human-robot systems able to leverage the strengths of both humans and their robotic counterparts has been greatly sought after because of the foreseen, broad-ranging impact across industry and research. We believe the true potential of these systems cannot be reached unless the robot is able to act with a high level of autonomy, reducing the burden of manual tasking or teleoperation. To achieve this level of autonomy, robots must be able to work fluidly with its human partners, inferring their needs without explicit commands. This inference requires the robot to be able to detect and classify the heterogeneity of its partners. We propose a framework for learning from heterogeneous demonstration based upon Bayesian inference and evaluate a suite of approaches on a real-world dataset of gameplay from StarCraft II. This evaluation provides evidence that our Bayesian approach can outperform conventional methods by up to 12.8$%$.