Deep Learning for Classifying Potential Entangled Objects

In this research, we tackle the challenge of picking only one object from a randomly stacked pile where the objects can potentially be entangledEntangled. No solution has been proposed to solve this challenge due to the complexity of picking one and only one object from the bin of entangledEntangled objects. Therefore, we propose a method for avoiding the situation where a robot picks multiple objects. In our proposed method, first, grasping pose candidates are computed by using the graspability index. Then, a Convolutional Neural Network (CNN) is trainedConvolutional Neural Network (CNN)Deep learning to predict whether or not the robot can pick one and only one object from the bin. Additionally, since a physics simulator is used to collect data to train the CNN, an automatic picking system can be built. The effectiveness of the proposed method is confirmed through experiments on robot Nextage and compared with previous bin-picking methods.