A Neural Network-based Framework for Fast and Smooth Posture Reconstruction of a Soft Continuum Arm
<|reference_start|>A Neural Network-based Framework for Fast and Smooth Posture Reconstruction of a Soft Continuum Arm: A neural network-based framework is developed and experimentally demonstrated for the problem of estimating the shape of a soft continuum arm (SCA) from noisy measurements of the pose at a finite number of locations along the length of the arm. The neural network takes as input these measurements and produces as output a finite-dimensional approximation of the strain, which is further used to reconstruct the infinite-dimensional smooth posture. This problem is important for various soft robotic applications. It is challenging due to the flexible aspects that lead to the infinite-dimensional reconstruction problem for the continuous posture and strains. Because of this, past solutions to this problem are computationally intensive. The proposed fast smooth reconstruction method is shown to be five orders of magnitude faster while having comparable accuracy. The framework is evaluated on two testbeds: a simulated octopus muscular arm and a physical BR2 pneumatic soft manipulator.<|reference_end|>
@article{wang2024a,
    title={A Neural Network-based Framework for Fast and Smooth Posture
  Reconstruction of a Soft Continuum Arm},
    author={Tixian Wang, Heng-Sheng Chang, Seung Hyun Kim, Jiamiao Guo, Ugur
  Akcal, Benjamin Walt, Darren Biskup, Udit Halder, Girish Krishnan, Girish
  Chowdhary, Mattia Gazzola, Prashant G. Mehta},
    journal={arXiv preprint arXiv:2409.12443},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.12443},
    primaryClass={cs.RO}
}