Kernkonzepte
This research proposes a novel three-layered architecture for achieving stylistic, human-like walking in humanoid robots, combining a Deep Neural Network (DNN) for trajectory generation with a Model Predictive Controller (MPC) for online step adjustment and dynamic feasibility.
Zusammenfassung
Bibliographic Information:
Romualdi, G., Viceconte, P. M., Moretti, L., Sorrentino, I., Dafarra, S., Traversaro, S., & Pucci, D. (2024). Online DNN-driven Nonlinear MPC for Stylistic Humanoid Robot Walking with Step Adjustment. 2024 IEEE-RAS International Conference on Humanoid Robots. IEEE.
Research Objective:
This research aims to develop a robust and adaptable locomotion control architecture for humanoid robots that enables stylistic, human-like walking while maintaining dynamic stability and handling external disturbances.
Methodology:
The researchers propose a three-layered hierarchical architecture:
- Trajectory Generation Layer: A Mode-Adaptive Neural Network (MANN) trained on human motion capture data generates nominal joint trajectories, base poses, and footstep plans, mimicking human-like walking styles.
- Trajectory Adjustment Layer: This layer, implemented as either a Receding Horizon Planner (RHP) or a Model Predictive Controller (MPC), ensures dynamic feasibility by adjusting the CoM trajectory and contact locations based on the nominal references from the DNN and real-time feedback. A Kalman Filter (KF) with genetically optimized parameters is used to reduce noise in joint velocity estimations for the MPC.
- Trajectory Control Layer: This layer generates joint commands for the robot based on the adjusted trajectories, utilizing a CoM-ZMP controller, a swing foot planner, and a QP-based inverse kinematics solver.
Key Findings:
- The proposed architecture successfully enabled the ergoCub humanoid robot to achieve stylistic walking patterns resembling human motion capture data.
- Both RHP and MPC implementations of the trajectory adjustment layer demonstrated the ability to adjust footsteps online to maintain balance under external disturbances.
- The MPC implementation, coupled with the GA-tuned KF, proved more effective in handling disturbances due to its closed-loop nature and reduced noise sensitivity.
Main Conclusions:
The research demonstrates the effectiveness of integrating data-driven and model-based approaches for achieving robust and stylistic humanoid robot locomotion. The proposed architecture, particularly the MPC implementation, shows promise for real-world applications requiring adaptability and disturbance rejection.
Significance:
This research contributes to the advancement of humanoid robot locomotion control by bridging the gap between stylistic motion generation and dynamic stability. The proposed architecture offers a promising solution for developing robots capable of navigating complex environments with human-like agility and adaptability.
Limitations and Future Research:
- The current system lacks a dedicated base estimator, potentially limiting its robustness to stronger disturbances.
- The use of position control instead of torque control might hinder the robot's ability to respond naturally to perturbations.
- Future research will focus on integrating a base estimator and transitioning to torque control to enhance the system's robustness and responsiveness.
Statistiken
The humanoid robot used is 160 cm tall and weighs 56 kg.
The DNN was trained on 20 minutes of human walking motion capture data, resulting in 150k training points.
The MPC operates with a sampling time of 50 ms and a 1.2 s horizon.
The RHP operates with a sampling time of 60 ms and a 1.2 s horizon.
The trajectory control layer runs at 500 Hz.
The robot withstood disturbances up to 68 N.