Learning Task Specifications from Demonstrations

Abstract

Real-world applications often naturally decompose into several sub-tasks. In many settings (e.g., robotics) demonstrations provide a natural way to specify the sub-tasks. However, most methods for learning from demonstrations either do not provide guarantees that the artifacts learned for the sub-tasks can be safely recombined or limit the types of composition available. Motivated by this deficit, we consider the problem of inferring Boolean non-Markovian rewards (also known as logical trace properties or specifications) from demonstrations provided by an agent operating in an uncertain, stochastic environment. Crucially, specifications admit well-defined composition rules that are typically easy to interpret. In this paper, we formulate the specification inference task as a maximum a posteriori (MAP) probability inference problem, apply the principle of maximum entropy to derive an analytic demonstration likelihood model and give an efficient approach to search for the most likely specification in a large candidate pool of specifications. In our experiments, we demonstrate how learning specifications can help avoid common problems that often arise due to ad-hoc reward composition.

Publication
In 32nd International Conference on Neural Information Processing Systems 2018
Marcell J. Vazquez-Chanlatte
Marcell J. Vazquez-Chanlatte
(student at University of California, Berkeley)
Susmit Jha
Susmit Jha
Technical Director, NuSCI

My research interests include artificial intelligence, formal methods, machine learning and dynamical systems.

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