Deformable Linear Object Prediction Using Locally Linear Latent Dynamics
This addresses a specific robotics manipulation task, but it is incremental as it builds on existing latent dynamics methods.
The paper tackles the problem of predicting the dynamics of deformable linear objects like rope, which is challenging due to non-linearities and infinite-dimensional spaces, and achieves accurate prediction up to ten steps into the future.
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.