Tool Shape Optimization through Backpropagation of Neural Network
<|reference_start|>Tool Shape Optimization through Backpropagation of Neural Network: 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.<|reference_end|>
@article{kawaharazuka2024tool,
    title={Tool Shape Optimization through Backpropagation of Neural Network},
    author={Kento Kawaharazuka and Toru Ogawa and Cota Nabeshima},
    journal={arXiv preprint arXiv:2407.12202},
    year={2024},
    doi={10.1109/IROS45743.2020.9341583},
    archivePrefix={arXiv},
    eprint={2407.12202},
    primaryClass={cs.RO}
}