BoxTwin: Learning Elastoplastic Articulated Object Dynamics from Videos
Authors: Heng Zhang, Gehan Zheng, Kaifeng Zhang, Jay Song, Shivansh Patel, Sonny Hu, Yunzhu Li, Changxi Zheng, +1 more
Organizations: Heng Zhang, Gehan Zheng, Kaifeng Zhang, Jay Song, Shivansh Patel, Sonny Hu, Yunzhu Li and Changxi Zheng are with SceniX, Inc. · Peter Yichen Chen is with the University of British Columbia.
Digital twins enable robots to anticipate and adapt to physical interactions, but existing models struggle with elastoplastic articulated objects (EAOs) that exhibit nonlinear elasticity, plastic yielding, and damage accumulation. We present BoxTwin, an interactive digital twin framework that learns the full dynamics of EAOs from videos. Our pipeline reconstructs the scene, identifies a physics aware constitutive model for each EAO. Experiments on manual folding and dual arm manipulation of EAOs show that BoxTwin accurately tracks joint trajectories and reproduces post contact plastic behavior over long horizons. By integrating video driven reconstruction with elastoplastic damage modeling, BoxTwin advances digital twins toward predictive, adaptive control of deformable articulated objects in unstructured environments.