Learning over time using a neuromorphic adaptive control algorithm for robotic arms
This work addresses the limitation of traditional control algorithms in adapting to new and dynamic environments for robotic arms, though it appears incremental as it builds on existing SNN methods.
The paper tackled the problem of robotic arm control in dynamic environments by deploying a Spiking Neural Network (SNN)-based adaptive control algorithm, resulting in the robot arm learning to complete tasks 15% faster in specific scenarios with random target points.
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.