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.

翻译：在本文中,我们探索了机器人臂通过部署和彻底评价基于Spiking神经网络 SNN的适应性控制算法,学习其最终效应能够达到的位置(x、y、z)所定义的基本操作空间的能力。虽然机器人传统控制算法在适应新的和动态的环境方面都有局限性,但我们显示机器人臂可以学习操作空间,并随着时间的推移更快地完成任务。我们还证明基于 SNNs 的适应性机器人控制算法可以在保持能源效率的同时作出快速反应。我们通过对适应性算法参数空间进行广泛搜索,并对不同的 SNN网络大小、学习率、动态机器人臂轨迹和响应时间进行评估,从而取得了这些结果。我们显示机器人臂在特定实验情景中学会更快完成15%的任务,比如有六九个随机目标点的情景。