  Computation, mechanics and materials merge in biological systems, which can
continually self-optimize through internal adaptivity across length scales,
from cytoplasm and biofilms to animal herds. Recent interest in such
material-based computation uses the principles of energy minimization, inertia
and dissipation to solve optimization problems. Although specific computations
can be performed using dynamical systems, current implementations of material
computation lack the ability to self-learn. In particular, the inverse problem
of designing self-learning mechanical systems which can use physical
computations to continuously self-optimize remains poorly understood. Here we
introduce the concept of self-learning mechanical circuits, capable of taking
mechanical inputs from changing environments and constantly updating their
internal state in response, thus representing an entirely mechanical
information processing unit. Our circuits are composed of a new mechanical
construct: an adaptive directed spring (ADS), which changes its stiffness in a
directional manner, enabling neural network-like computations. We provide both
a theoretical foundation and experimental realization of these elastic learning
units and demonstrate their ability to autonomously uncover patterns hidden in
environmental inputs. By implementing computations in an embodied physical
manner, the system directly interfaces with its environment, thus broadening
the scope of its learning behavior. Our results pave the way towards the
construction of energy-harvesting, adaptive materials which can autonomously
and continuously sense and self-optimize to gain function in different
environments.
