A Causal Bayesian Network and Probabilistic Programming Based Reasoning Framework for Robot Manipulation Under Uncertainty

Robot object manipulation in real-world environments is challenging because robot operation must be robust to a range of sensing, estimation, and actuation uncertainties to avoid potentially unsafe and costly mistakes that are a barrier to their adoption. In this paper, we propose a flexible and gen...

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Bibliographic Details
Published inarXiv.org
Main Authors Cannizzaro, Ricardo, Groom, Michael, Routley, Jonathan, Ness, Robert Osazuwa, Kunze, Lars
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 03.10.2024
Subjects
Actuators
Bayesian analysis
Decision making
Physics
Probabilistic models
Reasoning
Robots
Robustness
System dynamics
Uncertainty
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Summary:Robot object manipulation in real-world environments is challenging because robot operation must be robust to a range of sensing, estimation, and actuation uncertainties to avoid potentially unsafe and costly mistakes that are a barrier to their adoption. In this paper, we propose a flexible and generalisable physics-informed causal Bayesian network (CBN) based framework for a robot to probabilistically reason about candidate manipulation actions, to enable robot decision-making robust to arbitrary robot system uncertainties -- the first of its kind to use a probabilistic programming language implementation. Using experiments in high-fidelity Gazebo simulation of an exemplar block stacking task, we demonstrate our framework's ability to: (1) predict manipulation outcomes with high accuracy (Pred Acc: 88.6%); and, (2) perform greedy next-best action selection with 94.2% task success rate. We also demonstrate our framework's suitability for real-world robot systems with a domestic robot. Thus, we show that by combining probabilistic causal modelling with physics simulations, we can make robot manipulation more robust to system uncertainties and hence more feasible for real-world applications. Further, our generalised reasoning framework can be used and extended for future robotics and causality research.
ISSN:2331-8422