Learning to Propagate Interaction Effects for Modeling Deformable Linear Objects Dynamics

Modeling dynamics of deformable linear objects (DLOs), such as cables, hoses, sutures, and catheters, is an important and challenging problem for many robotic manipulation applications. In this paper, we propose the first method to model and learn full 3D dynamics of DLOs from data. Our approach is capable of capturing the complex twisting and bending dynamics of DLOs and allows local effects to propagate globally. To this end, we adapt the interaction network (IN) dynamics learning method for capturing the interaction between neighboring segments in a DLO and augment it with a recurrent model for propagating interaction effects along the length of a DLO. For learning twisting and bending dynamics in 3D, we also introduce a new suitable representation of DLO segments and their relationships. Unlike the original IN method, our model learns to propagate the effects of local interaction between neighboring segments to each segment in the chain within a single time step, without the need for iterated propagation steps. Evaluation of our model with synthetic and newly collected real-world data shows better accuracy and generalization in short-term and long- term predictions than the current state of the art. We further integrate our learned model in a model predictive control scheme and use it to successfully control the shape of a DLO. Our implementation is available at https://gitsvn-nt.oru.se/ammlab–public/in–bilstm.