Learning by doing

This paper investigates a novel approach for continuous gravity compensation in robotic systems using a Lagrangian Neural Network (LNN)-based Computed Torque Controller (CTC). Specifically, we use LNNs to model the system's dynamics and adaptively improve the performance of the CTC. Unlike traditional methods relying on predefined models or extensive offline training, this approach enables the controller to learn and adapt in real-time, without prior model knowledge, and within seconds during operation. The proposed approach focuses on energy-based representations and uses Lagrangian mechanics to ensure that the learned dynamics are physically interpretable and reliable, reducing the need for extensive training datasets. The LNN-CTC utilizes an online learning mechanism to continuously update the LNN-based on real-time data, ensuring accurate gravity compensation without prior model knowledge. Real-world experiments on a humanoid robotic arm with 4 degrees of freedom show that the LNN-based online learning CTC effectively compensates for gravity and adapts to changes in mass within 25s. We compare the proposed controller to a conventional model-based controller that relies on precise parameter knowledge and demonstrate that the LNN-CTC achieves similar trajectory tracking accuracy with significantly less data, no prior knowledge, and rapid adaptation to dynamic changes, such as added payloads without requiring parameter identification. This work contributes an adaptive real-time control framework that compensates for gravity. It overcomes high modeling efforts and large datasets, showing promise for scalability and generalization in autonomous systems and advanced robotics in uncertain environments.