Neural Models and Algorithms for Sensorimotor Control of an Octopus Arm
<|reference_start|>Neural Models and Algorithms for Sensorimotor Control of an Octopus Arm: In this article, a biophysically realistic model of a soft octopus arm with internal musculature is presented. The modeling is motivated by experimental observations of sensorimotor control where an arm localizes and reaches a target. Major contributions of this article are: (i) development of models to capture the mechanical properties of arm musculature, the electrical properties of the arm peripheral nervous system (PNS), and the coupling of PNS with muscular contractions; (ii) modeling the arm sensory system, including chemosensing and proprioception; and (iii) algorithms for sensorimotor control, which include a novel feedback neural motor control law for mimicking target-oriented arm reaching motions, and a novel consensus algorithm for solving sensing problems such as locating a food source from local chemical sensory information (exogenous) and arm deformation information (endogenous). Several analytical results, including rest-state characterization and stability properties of the proposed sensing and motor control algorithms, are provided. Numerical simulations demonstrate the efficacy of our approach. Qualitative comparisons against observed arm rest shapes and target-oriented reaching motions are also reported.<|reference_end|>
@article{wang2024neural,
    title={Neural Models and Algorithms for Sensorimotor Control of an Octopus Arm},
    author={Tixian Wang, Udit Halder, Ekaterina Gribkova, Rhanor Gillette, Mattia
  Gazzola, Prashant G. Mehta},
    journal={arXiv preprint arXiv:2402.01074},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.01074},
    primaryClass={eess.SY cs.RO cs.SY physics.bio-ph}
}