Octopus arm movements unveiled: a computational modeling approach to muscle activation patterns
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The octopus arm is a highly versatile appendage that can deform in any direction to perform various tasks. This flexibility arises from a densely packed muscular hydrostat whose coordinated activation generates a rich repertoire of movements, making it an important paradigm in the field of soft robotics and biomechanics. Understanding the neural and motor control of the octopus arm for developing bio-inspired applications is exciting but challenging, as the dexterity of the octopus arm is not accurately captured by existing models. This study presents a general modeling framework of the octopus arm muscular hydrostat, unveiling the principles of muscle activation that govern octopus arm movements. To develop this framework, we model the anatomical muscle architecture of the arm in a soft-tissue finite element solver lifex. The octopus arm is modeled through the continuum elastodynamics equations with a nonlinear anisotropic constitutive law, where an active stress tensor incorporates an anatomically detailed description of distinct muscle bundles. The fundamental deformations and complex movements are driven by muscle activation sequences derived from known EMG patterns, while the surrounding fluid environment is approximated through a surrogate, low-dimensional model characterized by tangential and normal damping on the arm. The model reproduces canonical arm movements observed in vivo and identifies the underlying muscle activation patterns responsible for each movement, such as reaching and fetching motion. Furthermore, the model also generates muscle activation patterns that are mechanically feasible but biologically implausible, providing a principled means to falsify muscle activation strategies. Our results demonstrate the tight coupling between muscle architecture, activation patterns, arm deformations and interaction with the surrounding fluid. The results highlight the motor simplifications made by the octopus to control its high-dimensional behavior, forming a basis for developing digital twins of biological systems and providing a robust tool for the design, fabrication, and control of next-generation soft robots.
