Rohan R. Paleja

Reinforcement learning (RL) has achieved tremendous success as a general framework for learning how to make decisions. Expand

We propose a personalized apprenticeship learning framework that automatically infers a representation of all human task demonstrators by extracting a human-specific embedding. Expand

We propose a personalized and interpretable apprenticeship scheduling algorithm that infers an interpretable representation of all human task demonstrators by extracting decision-making criteria via an inferred, personalized embedding non-parametric in the number of demonstrator types. Expand

The development of human-robot systems able to leverage the strengths of both humans and robotic counterparts has been greatly sought after because of the foreseen, broad-ranging impact across industry and research. Expand

We propose a Bayesian LfD framework to infer an integrated representation of all human task demonstrators by inferring human-specific embeddings, thereby distilling their unique characteristics. Expand

Learning from Demonstration (LfD) seeks to democratize robotics by enabling non-roboticist end-users to teach robots to perform a task by providing a human demonstration. Expand