Research Goal: My overarching research ambition is to create robust, versatile agents capable of navigating and resolving tasks in unstructured environments. To this end, I am drawn to several key areas:
Robotics, Reinforcement Learning, Machine Learning
We address learning from constrained demonstrators by propagating goal proximity rewards to out-of-distribution
states via confidence-based filtering and interpolation.
we introduce a self-supervised reward adaptation method for adapting policies without human assistance. It
Exceeds all other adaptation methods in manipulation and locomotion environment adaptation.
