A Generic Continuous Multi-Joint Spinal Robotic System for Agile and Accurate Behaviors with GNN-MPC method

The biomimetic research of vertebrates is challenging in both mechanism design and control methods.Motivated by natural acrobatics exhibited by cats and humans, this paper presents a generic multi-joint continuous spinal system and a learning-based algorithm for agile and accurate control.The spinal system combines flexibility with a high load-bearing capacity, rendering it suitable for various types of bionic robots.It features a chain-like structure formed by multiple pairs of spherical gear joints, which endow it with the ability to bend in all directions.Then, to realize dynamic and precious control, a universal control framework integrating online and offline learning is proposed.In this framework, Graph Neural Networks are employed to learn the dynamic model parameters of the spine offline, while the parameterized Model Predictive Control (GNN-MPC) can update the dynamic constraints online and select the optimal control strategy.In the aerial flipping task of the spinal column, a dynamic constraint analysis of the angular momentum of the spinal structure is conducted to derive the most efficient flipping strategy.It allows the spinal structure to execute flips in the air without relying on external forces or mechanical structures.Quantitative analyses of highload applications on the spine reveal that the spinal column can maintain strength, precision and flexibility simultaneously.A series of aerial flipping experiments prove the designed spine's scalability, flexibility and high load capacity.With GNN-MPC, the spine system can realistically mimic biological spine behavior, validating the algorithm's effectiveness and robustness.