Industrial Bin Picking of Potential Entangled Objects in Dense Clutter by Skeletonized Shape Restoration

Mizuki Takasu, Xinyi Zhang, Yukiyasu Domae, Weiwei Wan, Kensuke Harada

Abstract: Complex-shaped objects, e.g., tangled-prone or flexible objects, pose challenges in industrial bin picking. Due to the heavy occlusion and the elusive entanglement situation when these objects are randomly placed in a bin, it is difficult for the robot to pick only one object at a time. Previous works address this issue by abstracting away state estimation for the clutter and directly predicting the potentially entangled objects from visual input, making the entanglement estimation lack the proper geometrical information of the objects. In this paper, we propose a novel bin-picking system that can grasp avoiding entanglement for both rigid and flexible objects. Our method can (1) infer the full state of the objects in the clutter by skeletonizing and restoring the occluded shapes and (2) estimate the degree of entanglement quantitatively for each restored object and determine the grasping object based on the degree of entanglement, height information and the restoration rate. To evaluate our method, we perform real-world experiments using rigid objects with different shapes and one deformable object. Experimental results also demonstrate the effectiveness of our method on various objects with fixed or irregular geometries.