To Örebro University

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.

Robots manipulating deformable linear objects (DLOs) – such as surgical sutures in medical robotics, or cables and hoses in industrial assembly – can benefit substantially from accurate and fast differentiable predictive models. However, the off-the-shelf analytic physics models fall short of differentiability. Recently, neural-network-based data-driven models have shown promising results in learning DLO dynamics. These models have additional advantages compared to analytic physics models, as they are differentiable and can be used in gradient-based trajectory planning. Still, the data-driven approaches demand a large amount of training data, which can be challenging for real-world applications. In this paper, we propose a framework for learning a differentiable data-driven model for DLO dynamics with a minimal set of real-world data. To learn DLO twisting and bending dynamics in a 3D environment, we first introduce a new suitable DLO representation. Next, we use a recurrent network module to propagate effects between different segments along a DLO, thereby addressing a critical limitation of current state-of-the-art methods. Then, we train a data-driven model on synthetic data generated in simulation, instead of foregoing the time-consuming and laborious data collection process for real-world applications. To achieve a good correspondence between real and simulated models, we choose a set of simulation model parameters through parameter identification with only a few trajectories of a real DLO required. We evaluate several optimization methods for parameter identification and demonstrate that the differential evolution algorithm is efficient and effective for parameter identification. In DLO shape control tasks with a model-based controller, the data-driven model trained on synthetic data generated by the resulting models performs on par with the ones trained with a comparable amount of real-world data which, however, would be intractable to collect.

Traditional approaches to manipulating the state of deformable linear objects (DLOs) - i.e., cables, ropes - rely on model-based planning. However, constructing an accurate dynamic model of a DLO is challenging due to the complexity of interactions and a high number of degrees of freedom. This renders the task of achieving a desired DLO shape particularly difficult and motivates the use of model-free alternatives, which while maintaining generality suffer from a high sample complexity. In this paper, we bridge the gap between these fundamentally different approaches and propose a framework that learns dynamic models of DLOs through trial-and-error interaction. Akin to model-based reinforcement learning (RL), we interleave learning and exploration to solve a 3D shape control task for a DLO. Our approach requires only a fraction of the interaction samples of the current state-of-the-art model-free RL alternatives to achieve superior shape control performance. Unlike offline model learning, our approach does not require expert knowledge for data collection, retains the ability to explore, and automatically selects relevant experience.

Tracking of deformable linear objects (DLOs) is important for many robotic applications. However, achieving robust and accurate tracking is challenging due to the lack of distinctive features or appearance on the DLO, the object's high-dimensional state space, and the presence of occlusion. In this letter, we propose a method for tracking the state of a DLO by applying a particle filter approach within a lower-dimensional state embedding learned by an autoencoder. The dimensionality reduction preserves state variation, while simultaneously enabling a particle filter to accurately track DLO state evolution with a practically feasible number of particles. Compared to previous works, our method requires neither running a high-fidelity physics simulation, nor manual designs of constraints and regularization. Without the assumption of knowing the initial DLO state, our method can achieve accurate tracking even under complex DLO motions and in the presence of severe occlusions.

Tracking deformable linear objects (DLOs) is a key element for applications where robots manipulate DLOs. However, the lack of distinctive features or appearance on the DLO and the object’s high-dimensional state space make tracking challenging and still an open question in robotics. In this paper, we propose a method for tracking the state of a DLO by applying a particle filter approach, where the posterior probability of each sample is estimated by a learned predictor. Our method can achieve accurate tracking even with no prerequisite segmentation which many related works require. Due to the differentiability of the posterior probability predictor, our method can leverage the gradients of posterior probabilities with respect to the latent states to improve the motion model in the particle filter. The preliminary experiments suggest that the proposed method can provide robust tracking results and the estimated DLO state converges quickly to the true state if the initial state is unknown.