  For assistive robots and virtual agents to achieve ubiquity, machines will
need to anticipate the needs of their human counterparts. The field of Learning
from Demonstration (LfD) has sought to enable machines to infer predictive
models of human behavior for autonomous robot control. However, humans exhibit
heterogeneity in decision-making, which traditional LfD approaches fail to
capture. To overcome this challenge, 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. We
validate our approach is able to outperform state-of-the-art techniques on both
synthetic and real-world data sets.
