Language Conditioned Imitation Learning

Throughout my academic career, I have tried to bridge the gap between cognition and robotics by using modern approaches to artificial intelligence, specifically by enhancing imitation learning with natural language processing. Natural language is a critical part of efficient human-human interactions. Despite that, models of human-robot teaming tend to avoid the use of natural languages due to its inherent complexity and ambiguities. My approach is to teach novel tasks to robots by directly translating natural language into low-level control policies leveraging deep neural networks. Using this approach, robots will be enabled to quickly learn more complex tasks and their relationships from simpler previously learned motions like reaching, grasping, turning, and inserting. Additionally, the translation approach allows for easy knowledge transfer between different robots from a mutual task representation.


  • Research Project at ASU