  Legged robots operating in real-world environments must possess the ability
to rapidly adapt to unexpected conditions, such as changing terrains and
varying payloads. This paper introduces the Synaptic Motor Adaptation (SMA)
algorithm, a novel approach to achieving real-time online adaptation in
quadruped robots through the utilization of neuroscience-derived rules of
synaptic plasticity with three-factor learning. To facilitate rapid adaptation,
we meta-optimize a three-factor learning rule via gradient descent to adapt to
uncertainty by approximating an embedding produced by privileged information
using only locally accessible onboard sensing data. Our algorithm performs
similarly to state-of-the-art motor adaptation algorithms and presents a clear
path toward achieving adaptive robotics with neuromorphic hardware.
