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

@article{Supic2022LearningOT,
  title={Learning over time using a neuromorphic adaptive control algorithm for robotic arms},
  author={Lazar Supic and Terrence C. Stewart},
  journal={ArXiv},
  year={2022},
  volume={abs/2210.01243}
}
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… 

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