Bayesian Inference Of Temporal Task Specifications From Demonstrations

Authors:
Ankit Shah Massachusetts Institute of Technology
Pritish Kamath MIT
Julie A Shah MIT
Shen Li MIT

Introduction:

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.Inspired by this, the authors present Bayesian specification inference, a probabilistic model for inferring task specification as a temporal logic formula.

Abstract:

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 an 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 true specifications with over 90% similarity between the inferred specification and the ground truth, both within a synthetic domain and a real-world table setting task.

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