Generating and Transferring Priors for Causal Bayesian Network Parameter Estimation in Robotic Tasks

Robots acting in human environments will often face new situations and can benefit from transferring prior experience. Priors could enable robots to handle new tasks zero-shot and help prevent failures, which can be particularly costly in real robot applications. Due to their interpretable nature, causal Bayesian Networks (CBN) are popular for modeling cause-effect relations between semantically meaningful environment features and their effects on action success. While the CBN structure is often intuitively transferable to a new context, its probability distribution might change, requiring data-intensive relearning. In this letter, we propose three strategies that utilize semantic similarity and relatedness between the variables of two CBNs to generate and transfer informed CBN distribution priors. We evaluate the parameter prior accuracy in five different transfer scenarios, including sim-2-real, transferring parameters to more complex tasks with a larger number of parameters and even between two different tasks, which is particularly challenging. We show that the priors lead to better distribution estimates, particularly under a limited amount of new experiments, and improve the robot's ability to predict and prevent action failures by up to 50%.