No abstract

We propose to incrementally learn the segmentation of a demonstrated task into subtasks and the individual subtask policies themselves simultaneously. Previous robot learning from demonstration techniques have either learned the individual subtasks in isolation, combined known subtasks, or used knowledge of the overall task structure to perform segmentation. Our infinite mixture of experts approach instead automatically infers an appropriate partitioning (number of subtasks and assignment of data points to each one) directly from the data. We illustrate the applicability of our technique by learning a suitable set of subtasks from the demonstration of a finite-state machine robot soccer goal scorer.

We are interested in transferring control policies for arbitrary tasks from a human to a robot. Using interactive demonstration via teloperation as our transfer scenario, we cast learning as statistical regression over sensor-actuator data pairs. Our desire for interactive learning necessitates algorithms that are incremental and realtime. We examine Locally Weighted Projection Regression, a popular robotic learning algorithm, and Sparse Online Gaussian Processes in this domain on one synthetic and several robot-generated data sets. We evaluate each algorithm in terms of function approximation, learned task performance, and scalability to large data sets.

Abstract-The canonical Robot Learning from Demonstration scenario has a robot observing human demonstrations of a task or behavior in a few situations, and then developing a generalized controller. Current work further refines the learned system, often to perform the task better than the human could. However, the underlying assumption is that the demonstrations are successful, and are appropriate to reproduce. We, instead, consider the possibility that the human has failed in their attempt, and their demonstration is an example of what not to do. Thus, instead of maximizing the similarity of generated behaviors to those of the demonstrators, we examine two methods that deliberately avoid repeating the human's mistakes.