  For many real-world robotics applications, robots need to continually adapt
and learn new concepts. Further, robots need to learn through limited data
because of scarcity of labeled data in the real-world environments. To this
end, my research focuses on developing robots that continually learn in dynamic
unseen environments/scenarios, learn from limited human supervision, remember
previously learned knowledge and use that knowledge to learn new concepts. I
develop machine learning models that not only produce State-of-the-results on
benchmark datasets but also allow robots to learn new objects and scenes in
unconstrained environments which lead to a variety of novel robotics
applications.
