Deformable Linear Object Prediction Using Locally Linear Latent Dynamics

2021/03/26 by Wenbo Zhang, Karl Schmeckpeper, Zhang, Wenbo +5 · 1 citation
Computer Science · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Human Motion and Animation #Human Pose and Action Recognition #Multimodal Machine Learning Applications

paper · pdf · doi:10.48550/arxiv.2103.14184

openalex publication_date 2021/03/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We propose a framework for deformable linear object prediction. Prediction of deformable objects (e.g., rope) is challenging due to their non-linear dynamics and infinite-dimensional configuration spaces. By mapping the dynamics from a non-linear space to a linear space, we can use the good properties of linear dynamics for easier learning and more efficient prediction. We learn a locally linear, action-conditioned dynamics model that can be used to predict future latent states. Then, we decode the predicted latent state into the predicted state. We also apply a sampling-based optimization algorithm to select the optimal control action. We empirically demonstrate that our approach can predict the rope state accurately up to ten steps into the future and that our algorithm can find the optimal action given an initial state and a goal state.

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