Neuromechanical Autoencoders: Learning to Couple Elastic and Neural Network Nonlinearity

• URL: http://arxiv.org/abs/2302.00032v1
• Date: Tue, 31 Jan 2023 19:04:28 GMT
• Title: Neuromechanical Autoencoders: Learning to Couple Elastic and Neural Network Nonlinearity
• Authors: Deniz Oktay, Mehran Mirramezani, Eder Medina, Ryan P. Adams
• Abstract summary: We seek to develop machine learning analogs of. mechanical intelligence. We jointly learn the morphology of complex nonlinear elastic solids along with a. deep neural network to control it.
• Score: 15.47367187516723
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Intelligent biological systems are characterized by their embodiment in a complex environment and the intimate interplay between their nervous systems and the nonlinear mechanical properties of their bodies. This coordination, in which the dynamics of the motor system co-evolved to reduce the computational burden on the brain, is referred to as ``mechanical intelligence'' or ``morphological computation''. In this work, we seek to develop machine learning analogs of this process, in which we jointly learn the morphology of complex nonlinear elastic solids along with a deep neural network to control it. By using a specialized differentiable simulator of elastic mechanics coupled to conventional deep learning architectures -- which we refer to as neuromechanical autoencoders -- we are able to learn to perform morphological computation via gradient descent. Key to our approach is the use of mechanical metamaterials -- cellular solids, in particular -- as the morphological substrate. Just as deep neural networks provide flexible and massively-parametric function approximators for perceptual and control tasks, cellular solid metamaterials are promising as a rich and learnable space for approximating a variety of actuation tasks. In this work we take advantage of these complementary computational concepts to co-design materials and neural network controls to achieve nonintuitive mechanical behavior. We demonstrate in simulation how it is possible to achieve translation, rotation, and shape matching, as well as a ``digital MNIST'' task. We additionally manufacture and evaluate one of the designs to verify its real-world behavior.

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