Title: Tool Shape Optimization through Backpropagation of Neural Network
Authors: Kento Kawaharazuka, Toru Ogawa, Cota Nabeshima
Accepted at IROS2020
arxiv - https://arxiv.org/abs/2407.12202

When executing a certain task, human beings can choose or make an appropriate tool to achieve the task. This research especially addresses the optimization of tool shape for robotic tool-use. We propose a method in which a robot obtains an optimized tool shape, tool trajectory, or both, depending on a given task. The feature of our method is that a transition of the task state when the robot moves a certain tool along a certain trajectory is represented by a deep neural network. We applied this method to object manipulation tasks on a 2D plane, and verified that appropriate tool shapes are generated by using this novel method.