Reviews: Bayesian Inference of Temporal Task Specifications from Demonstrations

The authors introduce a probabilistic model for inferring task specification as a linear temporal logic (LTL) formula. This is encoded as three different behaviors, represented by LTL templates. The authors present linear chains, sets of LC and Forest of sub-tasks as prior distributions, as well as Complexity based and complexity independent domain-agnostic likelihood function. Given a set of demonstrations, the authors perform inference to obtain a posterior distribution over candidate formulas, which represent task specifications. The authors show that their method is able to recover accurate task specifications from demonstrations in both simulated domains and on a real-world dinner table domain. The authors provide a good background on LTL.