ArXiv

BoxTwin: Learning Elastoplastic Articulated Object Dynamics from Videos

2026-07-19 · 08:37 UTC ·Heng Zhang, Gehan Zheng, Kaifeng Zhang... ·1 min read
Authors
Heng Zhang, Gehan Zheng, Kaifeng Zhang...
Categories
cs.RO
arXiv
https://arxiv.org/abs/2607.17132v1
PDF
https://arxiv.org/pdf/2607.17132v1

Brief

BoxTwin presents a video-driven digital twin that reconstructs scenes and fits elastoplastic constitutive models per object to capture nonlinear elasticity, plastic yielding, and damage accumulation. Evaluated on manual folding and dual-arm manipulation, it accurately tracks joint kinematics and reproduces post-contact plastic deformation over long horizons, advancing predictive, adaptive control of deformable articulated objects.

Why it matters

BoxTwin (Heng Zhang et al., arXiv 2026-07-19) learns full elastoplastic articulated-object dynamics from videos, identifying physics-aware constitutive models that capture nonlinear elasticity, plastic yielding, and damage accumulation.

Key details

  • In experiments on manual folding and dual-arm manipulation, BoxTwin accurately tracks joint trajectories and reproduces post-contact plastic behavior over long horizons, enabling more predictive, adaptive control of deformable articulated objects.
Source evidence

Abstract

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.