论文标题
随着时间的流逝，使用神经臂的神经形态自适应控制算法学习
Learning over time using a neuromorphic adaptive control algorithm for robotic arms
论文作者
论文摘要
在本文中，我们探讨了机器人臂学习由（X，Y，Z）定义的基础操作空间，该位置（X，Y，Z）可以通过部署和彻底评估峰值神经网络SNN基于SNN的适应性控制算法来达到臂的最终效果，包括干扰。尽管适用于新的动态环境的传统控制算法在适应新的和动态的环境中都有局限性，但我们表明机器人臂可以随着时间的推移而更快地学习操作空间并完成任务。我们还证明，基于SNN的自适应机器人控制算法可以在保持能效的同时快速响应。我们通过对自适应算法参数空间进行广泛搜索，并评估不同SNN网络大小，学习率，动态机器人ARM轨迹和响应时间的算法性能来获得这些结果。我们表明，在特定的实验场景中，例如具有六个或九个随机目标点的方案，机器人臂学会了快速完成任务15％。
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.
