Human-in-the-Loop Machine Learning for Robot Navigation and Manipulation

While there have been huge advances in Machine Learning in recent years, many of the successes have relied on immense amounts of training data. Especially for sequential-decision-making tasks (the realm of reinforcement learning), obtaining such data from online experience can take a very long time. On the other hand, learning can often be dramatically accelerated by leveraging human input, for example as demonstrations of successful task executions, as interventions to correct mistakes, or simply as evaluative feedback separating “correct” actions from incorrect actions. This talk focuses on such Human-in-the-Loop Machine Learning for robotics tasks, covering both navigations, especially in tightly constrained spaces, and manipulation in open-world settings.