Deep Learning-Driven Modeling of Dynamic Acoustic Sensing in Biomimetic Soft-Robotic Pinnae

Biological function often depends on complex mechanisms of a dynamic, time-variant nature. An example are certain bat species (horseshoe bats - Rhinolophidae) that use intricate pinna musculatures to execute a variety of pinna deformations. While prior work has indicated a potential significance of these motions for sensory information encoding, it remains unclear how the complex time-variant pinna geometries could be controlled to enhance sensory performance. To address this issue, the present work has investigated deep neural network models as digital twins for biomimetic pinnae. The networks were trained to predict the acoustic impacts of the deformed pinna geometries. A total of three network architectures have been evaluated for this purpose using physical numerical simulations (boundary element method) as ground truth. The networks predicted the acoustic beampattern function from pinna shape or even directly from the states of actuators that were used to deform the pinna shapes in simulation. Inserting prior knowledge in the form of beam-shaped basis functions did not improve network performance. The ability of the networks to produce beampattern predictions with low computational effort (in about three milliseconds each) should lend itself readily to supporting learning methods such as deep reinforcement learning that require many such functional evaluations.