Supervised Bayesian specification inference from demonstrations

doi:10.1177/02783649231204659

Supervised Bayesian specification inference from demonstrations

2023-10-04

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DOI：10.1177/02783649231204659

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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.

Keywords:

Specification inference

learning from demonstrations

probabilistic models

Supervised Bayesian specification inference from demonstrations

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