Serv-Arm Robotic Manipulator Dataset for Xylo-Aware Spiking Action Selection

This dataset contains joint-space samples from the low-cost Serv-Arm robotic manipulator, organized for the development and evaluation of spiking neural network controllers and neuromorphic action-selection methods. Each sample includes the current configuration of four robotic joints, the desired goal configuration, and the corresponding next joint command. The model input therefore contains eight attributes: four current joint angles and four goal joint angles. For neuromorphic deployment, the original continuous joint-space transitions were converted into nine discrete action classes compatible with the input and output constraints of the Xylo Audio 3 neuromorphic processor: 1. hold 2. base_pos 3. base_neg 4. shoulder_pos 5. shoulder_neg 6. elbow_pos 7. elbow_neg 8. gripper_pos 9. gripper_neg The action label is derived from the dominant normalized joint displacement between the current joint configuration and the next joint command. If all joint displacements are below a predefined threshold, the sample is labeled as hold. Otherwise, the joint with the largest normalized displacement is selected, and the sign of the displacement determines the corresponding positive or negative action. The dataset is divided into training, validation, and test subsets. It includes both the original continuous joint-space representation and a processed representation containing the derived action labels, dominant-joint information, and normalized joint displacements. The processed dataset was used to train and evaluate a compact leaky integrate-and-fire spiking neural network with 8 input channels, 128 hidden neurons, and 9 output classes. The resulting model was evaluated using floating-point simulation, XyloSim, and a physical Xylo Audio 3 neuromorphic processor. The dataset preserves the original class distribution, which is highly imbalanced and reflects the frequency of actions generated from the original joint-space transitions. Researchers may apply class weighting, balanced sampling, or other imbalance-handling strategies during model training while preserving the original validation and test distributions. This dataset supports research in neuromorphic robotics, embedded artificial intelligence, robotic action selection, low-power inference, hardware-aware neural network deployment, simulation-to-hardware evaluation, and reproducible benchmarking of spiking neural networks.