Reasoning Operational Decisions for Robots via Time Series Causal Inference

Yu Cao, Boyang Li, Qian Li, Adam Stokes, David Ingram, Aristides Kiprakis

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Justifying operational decisions for robots is a challenging task as the operator or the robot itself has to understand the underlying physical interaction between the robot and the environment to predict the potential outcome. It is desirable to understand how the decision influences the operational performance in the way of causal relationship for the purpose of explainable decision-making. Here we propose a novel causal inference framework for the discovery and inference on the reasoning of the operational decisions for robots. It unifies both domain knowledge integration and model-free causal inference, allowing a data-driven causal knowledge learning on time series data. The framework is evaluated in the experiments of an underwater robot with complex environmental interactions. The results show that the framework can learn the causal structure and inference model to accurately explain and predict the operation performance with integrated physics.
Original languageEnglish
Title of host publication2021 IEEE International Conference on Robotics and Automation (ICRA)
Pages6124-6131
Number of pages8
DOIs
Publication statusPublished - 18 Oct 2021

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