  Robots capable of performing manipulation tasks in a broad range of missions
in unstructured environments can develop numerous applications to impact and
enhance human life. Existing work in robot learning has shown success in
applying conventional machine learning algorithms to enable robots for
replicating rather simple manipulation tasks in manufacturing, service and
healthcare applications, among others. However, learning robust and versatile
models for complex manipulation tasks that are inherently multi-faceted and
naturally intricate demands algorithmic advancements in robot learning. Our
research supports the long-term goal of making robots more accessible and
serviceable to the general public by expanding robot applications to real-world
scenarios that require systems capable of performing complex tasks. To achieve
this goal, we focus on identifying and investigating knowledge gaps in robot
learning of complex manipulation tasks by leveraging upon human-robot
interaction and robot learning from human instructions. This document presents
an overview of the recent research developments in the Persistent Autonomy and
Robot Learning (PeARL) lab at the University of Massachusetts Lowell. Here, I
briefly discuss different research directions, and present a few proposed
approaches in our most recent publications. For each proposed approach, I then
mention potential future directions that can advance the field.
