Muhan Hou

Active Robot Learning from Demonstrations with Human Teachers

This thesis investigates Active Learning from Demonstrations (Active LfD) as a principled approach to reduce distribution shift while accounting for human factors in realistic human-in-the-loop settings and develops an Active LfD algorithm that operates with offline demonstrations and guides the resulting demonstration distribution toward a more balanced one, or more generally toward any specified target distribution, so as to improve robot learning.