Safe Explicable Planning

Human expectations stem from their knowledge about the others and the world. Where human-AI interaction is concerned, such knowledge may be inconsistent with the ground truth, resulting in the AI agent not meeting its expectations and degraded team performance. Explicable planning was previously introduced as a novel planning approach to reconciling human expectations and the agent's optimal behavior for more interpretable decision-making. One critical issue that remains unaddressed is safety in explicable planning since it can lead to explicable behaviors that are unsafe. We propose Safe Explicable Planning (SEP) to extend the prior work to support the specification of a safety bound. The objective of SEP is to search for behaviors that are close to the human's expectations while satisfying the bound on the agent's return, the safety criterion chosen in this work. We show that the problem generalizes multi-objective optimization and our formulation introduces a Pareto set. Under such a formulation, we propose a novel exact method that returns the Pareto set of safe explicable policies, a more efficient greedy method that returns one of the Pareto optimal policies, and approximate solutions for them based on the aggregation of states to further scalability. Formal proofs are provided to validate the desired theoretical properties of the exact and greedy methods. We evaluate our methods both in simulation and with physical robot experiments. Results confirm the validity and efficacy of our methods for safe explicable planning.