1.  Multi-objective learning: given the objectives specified either by reward or demonstrations, 

how can we balance the different and possibly conflicting objectives from users? 
 

                 

                 

2.  Manipulating the assistive learning: a famous result from social choice theory is that, a non-

trivial collective decision is subject to manipulation [4], how easy is it for one or some users to 
change the behavior of an assistive agent?  Or how can a human bias the system towards their 
own interest? By studying how to manipulate assistive learning, the ultimate goal is still to 
develop robots that can delegate multiple humans’ interests fairly and correctly.
informatics-phd-projects-2022-23