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Robots operating in real-world environments must reason about possibleoutcomes of stochastic actions and make decisions based on partial observationsof the true world state. A major challenge for making accurate and robustaction predictions is the problem of confounding, which if left untreated canlead to prediction errors. The partially observable Markov decision process(POMDP) is a widely-used framework to model these stochastic andpartially-observable decision-making problems. However, due to a lack ofexplicit causal semantics, POMDP planning methods are prone to confounding biasand thus in the presence of unobserved confounders may produce underperformingpolicies. This paper presents a novel causally-informed extension of "anytimeregularized determinized sparse partially observable tree" (AR-DESPOT), amodern anytime online POMDP planner, using causal modelling and inference toeliminate errors caused by unmeasured confounder variables. We further proposea method to learn offline the partial parameterisation of the causal model forplanning, from ground truth model data. We evaluate our methods on a toyproblem with an unobserved confounder and show that the learned causal model ishighly accurate, while our planning method is more robust to confounding andproduces overall higher performing policies than AR-DESPOT.

CAR-DESPOT: Causally-Informed Online POMDP Planning for Robots in Confounded Environments