Strategic Jenga Play via Graph Based Dynamics Modeling
Kavya Puthuveetil, Xinyi Zhang, Kazuto Yokoyama, Tetsuya Narita

TL;DR
This paper introduces a graph-based modeling approach for strategic Jenga play, focusing on block selection and extraction by predicting tower stability and dynamics, with promising simulation results for contact-rich manipulation tasks.
Contribution
It presents a novel graph-based framework for modeling Jenga tower dynamics and structure, enabling strategic block selection and extraction in contact-rich manipulation.
Findings
Successful classification of tower stability for block removal
Effective dynamics prediction for safe block extraction
Promising simulation results on full-sized Jenga towers
Abstract
Controlled manipulation of multiple objects whose dynamics are closely linked is a challenging problem within contact-rich manipulation, requiring an understanding of how the movement of one will impact the others. Using the Jenga game as a testbed to explore this problem, we graph-based modeling to tackle two different aspects of the task: 1) block selection and 2) block extraction. For block selection, we construct graphs of the Jenga tower and attempt to classify, based on the tower's structure, whether removing a given block will cause the tower to collapse. For block extraction, we train a dynamics model that predicts how all the blocks in the tower will move at each timestep in an extraction trajectory, which we then use in a sampling-based model predictive control loop to safely pull blocks out of the tower with a general-purpose parallel-jaw gripper. We train and evaluate our…
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Taxonomy
TopicsArtificial Intelligence in Games
