Integration of DNN and MPC for Locomotion: The paper introduces a three-layer architecture combining a Deep Neural Network (DNN) for trajectory generation and a Model Predictive Controller (MPC) for trajectory adjustment. This system bridges data-driven and model-based approaches, enabling stylistic human-like walking with online step adjustments.
MPC with Control Barrier Function for Step Adjustment: The MPC ensures kinematically feasible center of mass (CoM) motion while adjusting steps. A control-barrier function is used to keep the CoM within a safe region, ensuring stability and robustness during locomotion, even under external disturbances.
GA-tuned Kalman Filter for Noise Reduction: The paper introduces a Genetic Algorithm-tuned Kalman filter to reduce noise in CoM velocity and angular momentum measurements, improving the smoothness and stability of the robotβs movements. The system was validated to withstand disturbances of up to 68 Newtons on the ergoCub robotβ.