Heuristic Adaptation of Potentially Misspecified Domain Support for Likelihood-Free Inference in Stochastic Dynamical Systems

The study on heuristic adaptation for likelihood-free inference (LFI) in robotics highlights the critical issue of support misspecification, which can produce suboptimal and misleading posteriors. To combat this, researchers proposed three heuristic methods—EDGE, MODE, and CENTRE—that dynamically adjust the support during the inference process. Evaluations showed that these adaptations lead to improved classification of length and stiffness in dynamic deformable linear objects (DLOs). Furthermore, when these refined posteriors are utilized in simulation-based policy learning, they result in more robust performance of object-centric robotic agents. This research not only addresses a fundamental challenge in LFI but also enhances the capabilities of robots in manipulating complex objects, marking a significant step forward in the field of robotics.