My research lies at the intersection of Robot Learning, Computer Vision, and Field Robotics. I am interested in self-supervised learning, reinforcement learning, and representation learning for fast adaptation and generalization in unstructured environments. My ultimate goal is to develop robots that probe when uncertain, and make mistakes only once, if at all.
September 2023: I will be joining UW as a PhD student in Robotics!
We present a formal framework and implementation in a cognitive agent for novelty handling and demonstrate the efficacy of the proposed methods for detecting and handling a large set of novelties in a crafting task in a simulated environment.
We introduce a unified framework for creative problem solving through action discovery. We describe two methods which enable action discovery at a declarative and neurosymbolic level, namely through action primitive segmentation, and behavior babbling, respectively.
We describe a method for discovering new action primitives through object exploration and action segmentation, which is able to iteratively update the robot's knowledge base on-the-fly until the solution becomes feasible.