  Imitation learning allows social robots to learn new skills from human
teachers without substantial manual programming, but it is difficult for
robotic imitation learning systems to generalize demonstrated skills as well as
human learners do. Contemporary neurocomputational approaches to imitation
learning achieve limited generalization at the cost of data-intensive training,
and often produce opaque models that are difficult to understand and debug. In
this study, we explore the viability of developing purely-neural controllers
for social robots that learn to imitate by reasoning about the underlying
intentions of demonstrated behaviors. We present NeuroCERIL, a brain-inspired
neurocognitive architecture that uses a novel hypothetico-deductive reasoning
procedure to produce generalizable and human-readable explanations for
demonstrated behavior. This approach combines bottom-up abductive inference
with top-down predictive verification, and captures important aspects of human
causal reasoning that are relevant to a broad range of cognitive domains. Our
empirical results demonstrate that NeuroCERIL can learn various procedural
skills in a simulated robotic imitation learning domain. We also show that its
causal reasoning procedure is computationally efficient, and that its memory
use is dominated by highly transient short-term memories, much like human
working memory. We conclude that NeuroCERIL is a viable neural model of
human-like imitation learning that can improve human-robot collaboration and
contribute to investigations of the neurocomputational basis of human
cognition.
