Supervised learning through physical changes in a mechanical system

Abstract

Significance Supervised learning algorithms can learn subtle features that distinguish one class of input examples from another. We explore a supervised training framework in which mechanical metamaterials physically learn to distinguish different classes of forces by exploiting plasticity and nonlinearities in the material. After a period of training with examples of forces, the material can respond correctly to previously unseen novel forces that share spatial correlation patterns with the training examples. Such generalization can allow mechanical parts of microelectronics and adaptive robotics to learn to distinguish patterns of force stimuli on the fly. Our work shows how learning and generalization are not restricted to software algorithms, but can naturally emerge from plasticity and nonlinearities in elastic materials.