High-Accuracy and Energy-Efficient Learning of Inverse Dynamics for High-DoF Robotic Manipulators Using Spiking Neural Networks

Inverse dynamics maps the joint space of a robotic system to the torque space, but traditional methods for solving it rely on direct physical modeling of robots, which is difficult or even impossible due to high nonlinearity and external disturbance. Data-driven methods address these issues by learning the complex and unknown inverse dynamics. One of the key features of inverse dynamics is temporal dependency because robot movements are continuous. While recent Artificial Neural Network (ANN) methods leverage this to improve prediction accuracy, their high computational cost limits use in edge computing scenarios. Neuromorphic computing based on Spiking Neural Network (SNN) features extremely low power consumption. However, existing neuromorphic approaches remain limited to 2 degrees-of-freedom (DoFs) due to insufficient temporal modeling and low scalability. This paper presents the first neuromorphic approach capable of accurately learning the inverse dynamics of up to 7 DoF robotic manipulators. The method utilizes a liquid state machine (LSM) with temporally structured inputs and a novel decoding strategy to better model the temporal dependency in robotic motion. To account for the distinct characteristics of each joint while enhancing the model's scalability, we adopt a multi-LSM structure, enabling individualized modeling for each joint. Our neuromorphic method outperforms state-of-the-art approaches by at least 14.6% while using only 40% of their power consumption. It achieves the highest prediction accuracy across four different robotic platforms and is further validated on a UR5 robotic manipulator in a trajectory tracking task.