  In this paper, we explore the ability of a robot arm to learn the underlying
operation space defined by the positions (x, y, z) that the arm's end-effector
can reach, including disturbances, by deploying and thoroughly evaluating a
Spiking Neural Network SNN-based adaptive control algorithm. While traditional
control algorithms for robotics have limitations in both adapting to new and
dynamic environments, we show that the robot arm can learn the operational
space and complete tasks faster over time. We also demonstrate that the
adaptive robot control algorithm based on SNNs enables a fast response while
maintaining energy efficiency. We obtained these results by performing an
extensive search of the adaptive algorithm parameter space, and evaluating
algorithm performance for different SNN network sizes, learning rates, dynamic
robot arm trajectories, and response times. We show that the robot arm learns
to complete tasks 15% faster in specific experiment scenarios such as scenarios
with six or nine random target points.
