Towards probabilistic causal discovery, inference & explanations for autonomous drones in mine surveying tasks

Conference item

Causal modelling offers great potential to provide autonomous agents the ability to understand the data-generation process that governs their interactions with the world. Such models capture formal knowledge as well as probabilistic representations of noise and uncertainty typically encountered by autonomous robots in real-world environments. Thus, causality can aid autonomous agents in making decisions and explaining outcomes, but deploying causality in such a manner introduces new challenges. Here we identify challenges relating to causality in the context of a drone system operating in a salt mine. Such environments are challenging for autonomous agents because of the presence of confounders, non-stationarity, and a difficulty in building complete causal models ahead of time. To address these issues, we propose a probabilistic causal framework consisting of: causally-informed POMDP planning, online SCM adaptation, and post-hoc counterfactual explanations. Further, we outline planned experimentation to evaluate the framework integrated with a drone system in simulated mine environments and on a real-world mine dataset.

Authors: Cannizzaro, R; Howard, R; Lewinska, P; Kunze, L

• Publication status: Accepted
• Peer review status: Peer reviewed
• Publication date: 2024-02-15
• Acceptance date: 2023-06-30
• Event title: IROS 2023 Workshop: Causality for Robotics: Answering the Question of Why
• Event location: Detroit, Michigan
• Event website: https://sites.google.com/view/iros23-causal-robots
• Event start date: 2023-10-05
• Event end date: 2023-10-05
• DOI: 10.48550/arxiv.2308.10047
• Language: English