Learning Inverse Kinematic with Multi-layered Graph Convolutional Network (MGCNN)

Inverse kinematics (IK) is a fundamental problem in robotics that involves computing the joint parameters necessary to achieve the desired position and orientation of a robotic manipulator's end-effector. Traditional methods for solving IK such as Jacobian-based solvers, pseudo-inverse methods, and numerical solvers such as Levenberg-Marquardt and Newton-Raphson are faced with serious challenges ranging from local minimum, singularity, computational cost, and the inability to handle highly redundant systems effectively. To address some of these problems, we proposed Multi-layered Graph Convolutional Neural Network (MGCNN) for 7-DOF (Degree-Of-Freedom) redundant manipulators. The architecture consists of 7 GCN (Graph Convolutional Network) blocks each block consists of a GCN layer, normalization, and activation functions followed by 2 fully connected layers for joint regression. A skip-connection mechanism was used to pool and concatenate outputs from all GCN blocks before being fed into the fully- connected (FC) layers of the network which ensures optimal feature utilization across all layers. The model was trained on mixed data (preplanned trajectories and random poses), where each pose of the robotic arm is represented as a graph with joints and kinematic links representing nodes and edges of the graph respectively. Node features consist of the global end-effector poses and local Denavit-Hartenberg (DH) parameters, enabling the network to learn both global and joint-specific relationships. Experimental results demonstrate strong generalization, a narrow confidence interval relative to Mean Absolute Error (MAE), low error susceptibility, and fast inference. The model achieves an MAE below 0.001 compared to ground truth, proving its robustness and efficiency.