SNN-Based Joint-Space Control of the Serv-Arm Robot on Low-Cost Embedded Hardware

This work introduces a neuromorphic joint-space controller for the 4-DoF Serv-Arm robotic manipulator, implemented via a Spiking Neural Network (SNN) on low-cost embedded hardware. Unlike traditional methods based on inverse kinematics or Cartesian-space control, the proposed architecture operates directly on servo joint angles, receiving current and target configurations to predict the following joint-space action. A synthetic dataset with over 500,000 samples was generated to comprehensively cover the robot’s workspace, including oversampling of mechanically challenging configurations. The SNN was trained with surrogate gradients (snnTorch), and a systematic grid search optimized the hidden layer size, learning rate, and membrane decay factor. The final model, featuring a 128-neuron LIF hidden layer, achieved a Mean Absolute Error (MAE) of 11.06° on the validation set and generated smooth, reproducible trajectories during real-robot execution. Additionally, this study includes an empirical energy comparison between the SNN controller and an ANN baseline on a Raspberry Pi under identical motion routines. The results indicate that both models consume similar average powers, with the SNN showing a modest increase due to the temporal simulation overhead typical of spiking models on standard CPUs.