セマンティックセグメンテーションを用いたCNNによる複数物体へのリーチング動作

抄録

Dual-arm robots are capable of handling objects as well as humans. Such robots have potential for supporting human life. For example, in order for a robot to pick up an object, it is necessary to autonomously generate the reaching motion toward the object. For this issue, we have thus far presented an End-to-End motion planner based on convolutional neural network, CNN. However, since a single object was assumed, the robot based on this motion planner fails to generate the reaching motion for multiple objects. For this challenge, we focus on an image segmentation technique, which is so-called semantic segmentation. In the learning phase based on CNN, segmented images of the reaching target are used as the input. Through the experiments, we show that the robot is able to generate the reaching motions toward multiple objects.