Learning over time using a neuromorphic adaptive control algorithm for robotic arms

TL;DR

This paper presents a neuromorphic adaptive control algorithm using Spiking Neural Networks that enables robotic arms to learn their operational space, adapt to disturbances, and improve task completion speed and energy efficiency over time.

Contribution

The paper introduces a novel SNN-based adaptive control algorithm for robotic arms, demonstrating improved learning speed and energy efficiency compared to traditional methods.

Findings

Robot arm learns operational space and completes tasks faster over time.

Adaptive control enables 15% faster task completion in specific scenarios.

Energy-efficient response maintained during learning process.

Abstract

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.