Vision Based Topological State Recognition for Deformable Linear Object Untangling Conducted in Unknown Background In this paper, we propose a deep learning based method to recognize the topological state of a deformable linear object (DLO). The utilization of deep learning can ensure that topological state recognition is robust to background change. This feature is useful if applications of DLO manipulation in real environment. And this feature has never be realized. In addition, the proposed scheme is also applied to the situation when multiple DLOs exist. This situation has never been considered. By integrating the proposed topological state recognition method and DLO untangling strategy, rope untangling experiments are conducted for both the situations of containing a single DLO and double DLOs.

Occlusionrobust Deformable Object Tracking without Physics Simulation Estimating the state of a deformable object is crucial for robotic manipulation, yet accurate tracking is challenging when the object is partiallyoccluded. To address this problem, we propose an occlusionrobust RGBD sequence tracking framework based on Coherent Point Drift (CPD). To mitigate the effects of occlusion, our method 1) Uses a combination of locally linear embedding and constrained optimization to regularize the output of CPD, thus enforcing topological consistency when occlusions create disconnected pieces of the object; 2) Reasons about the freespace visible by an RGBD sensor to better estimate the prior on point location and to detect tracking failures during occlusion; and 3) Uses shape descriptors to find the most relevant previous state of the object to use for tracking after a severe occlusion. Our method does not rely on physics simulation or a physical model of the object, which can be difficult to obtain in unstructured environments. Despite having no physical model, our experiments demonstrate that our method achieves improved accuracy in the presence of occlusion as compared to a physicsbased CPD method while maintaining adequate runtime.