ORIGINAL RESEARCH article

Front. Cognit.

Sec. Reason and Decision-Making

Volume 4 - 2025 | doi: 10.3389/fcogn.2025.1565059

This article is part of the Research TopicCausal Cognition in Humans and Machines - Volume IIView all 4 articles

Robot Pouring: Identifying Causes of Spillage and Selecting Alternative Action Parameters Using Probabilistic Actual Causation

Provisionally accepted
Jaime  MaldonadoJaime Maldonado1,2*Jonas  KrummeJonas Krumme1,2Christoph  ZetzscheChristoph Zetzsche1,2Vanessa  DidelezVanessa Didelez1,3Kerstin  SchillKerstin Schill1,2
  • 1University of Bremen, Bremen, Germany
  • 2Cognitive Neuroinformatics, Bremen, Germany
  • 3Department of Biometry and Data Management, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany

The final, formatted version of the article will be published soon.

In everyday life, we perform tasks (e.g., cooking or cleaning) that involve a large variety of objects and goals. When confronted with an unexpected or unwanted outcome, we take corrective actions and try again until achieving the desired result. The reasoning performed to identify a cause of the observed outcome and to select an appropriate corrective action is a crucial aspect of human reasoning for successful task execution. Central to this reasoning is the assumption that a factor is responsible for producing the observed outcome. In this paper, we investigate the use of probabilistic actual causation to determine whether a factor is the cause of an observed undesired outcome. Furthermore, we show how the actual causation probabilities can be used to find alternative actions to change the outcome. We apply the probabilistic actual causation analysis to a robot pouring task. When spillage occurs, the analysis indicates whether a task parameter is the cause and how it should be changed to avoid spillage. The analysis requires a causal graph of the task and the corresponding conditional probability distributions. To fulfill these requirements, we perform a complete causal modeling procedure (i.e., task analysis, definition of variables, determination of the causal graph structure, and estimation of conditional probability distributions) using data from a realistic simulation of the robot pouring task, covering a large combinatorial space of task parameters. Based on the results, we discuss the implications of the variables' representation and how the alternative actions suggested by the actual causation analysis would compare to the alternative solutions proposed by a human observer. The practical use of the analysis of probabilistic actual causation to select alternative action parameters is demonstrated.

Keywords: Robot pouring, causality, probabilistic actual causation, causal discovery, action-guiding explanations

Received: 22 Jan 2025; Accepted: 26 May 2025.

Copyright: © 2025 Maldonado, Krumme, Zetzsche, Didelez and Schill. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Jaime Maldonado, University of Bremen, Bremen, Germany

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