Probabilistic Specification Learning for Planning with Safety Constraints

This paper proposes a framework for learning task specifications from demonstrations, while ensuring that the learned specifications do not violate safety constraints. Furthermore, we show how these specifications can be used in a planning problem to control the robot under environments that can be different from those encountered during the learning phase. We formulate the specification learning problem as a grammatical inference problem, using probabilistic automata to represent specifications. The edge probabilities of the resulting automata represent the demonstrator's preferences. The main novelty in our approach is to incorporate the safety property during the learning process. We prove that the resulting automaton always respects a pre-specified safety property, and furthermore, the proposed method can easily be included in any Evidence-Driven State Merging (EDSM)-based automaton learning scheme. Finally, we introduce a planning algorithm that produces the most desirable plan by maximizing the probability of an accepting trace of the automaton. Case studies show that our algorithm learns the true probability distribution most accurately while maintaining safety. Since, specification is detached from the robot's environment model, a satisfying plan can be synthesized for a variety of different robots and environments including both mobile robots and manipulators.