Domain Concretization from Examples: Addressing Missing Domain Knowledge via Robust Planning

TL;DR

This paper introduces a method for addressing incomplete domain knowledge in robot planning by inferring missing information from examples and generating robust plans that succeed across multiple candidate models.

Contribution

It formulates Domain Concretization as an inverse problem to domain abstraction, proposing algorithms that improve planning success in incomplete domain models.

Findings

Increased plan success rate in IPC and robotics domains.

Robust plans generated with minimal impact on cost.

Sample-based and online search methods improve efficiency.

Abstract

The assumption of complete domain knowledge is not warranted for robot planning and decision-making in the real world. It could be due to design flaws or arise from domain ramifications or qualifications. In such cases, existing planning and learning algorithms could produce highly undesirable behaviors. This problem is more challenging than partial observability in the sense that the agent is unaware of certain knowledge, in contrast to it being partially observable: the difference between known unknowns and unknown unknowns. In this work, we formulate it as the problem of Domain Concretization, an inverse problem to domain abstraction. Based on an incomplete domain model provided by the designer and teacher traces from human users, our algorithm searches for a candidate model set under a minimalistic model assumption. It then generates a robust plan with the maximum probability of success under the set of candidate models. In addition to a standard search formulation in the model-space, we propose a sample-based search method and also an online version of it to improve search time. We tested our approach on IPC domains and a simulated robotics domain where incompleteness was introduced by removing domain features from the complete model. Results show that our planning algorithm increases the plan success rate without impacting the cost much.