  When observing task demonstrations, human apprentices are able to identify
whether a given task is executed correctly long before they gain expertise in
actually performing that task. Prior research into learning from demonstrations
(LfD) has failed to capture this notion of the acceptability of a task's
execution; meanwhile, temporal logics provide a flexible language for
expressing task specifications. Inspired by this, we present Bayesian
specification inference, a probabilistic model for inferring task specification
as a temporal logic formula. We incorporate methods from probabilistic
programming to define our priors, along with a domain-independent likelihood
function to enable sampling-based inference. We demonstrate the efficacy of our
model for inferring specifications, with over 90% similarity observed between
the inferred specification and the ground truth, both within a synthetic domain
and during a real-world table setting task.
