Hybrid LMC: Hybrid Learning and Model-based Control for Wheeled Humanoid Robot via Ensemble Deep Reinforcement Learning

Abstract

Model-based controllers using a linearized model around the system's equilibrium point is a common approach in the control of a wheeled humanoid due to their less computational load and ease of stability analysis. However, controlling a wheeled humanoid robot while it lifts an unknown object presents significant challenges, primarily due to the lack of knowledge in object dynamics. This paper presents a framework designed for predicting the new equilibrium point explicitly to control a wheeled-legged robot with unknown dynamics. We estimated the total mass and center of mass of the system from its response to initially unknown dynamics, then calculated the new equilibrium point accordingly. To avoid using additional sensors (e.g., force torque sensor) and reduce the effort of obtaining expensive real data, a data-driven approach is utilized with a novel real-to-sim adaptation. A more accurate nonlinear dynamics model, offering a closer representation of real-world physics, is injected into a rigid-body simulation for real-to-sim adaptation. The nonlinear dynamics model parameters were optimized using Particle Swarm Optimization. The efficacy of this framework was validated on a physical wheeled inverted pendulum, a simplified model of a wheeledlegged robot. The experimental results indicate that employing a more precise analytical model with optimized parameters significantly reduces the gap between simulation and reality, thus improving the efficiency of a model-based controller in controlling a wheeled robot with unknown dynamics.

Evolution, Design, and Future Trajectories on Bipedal Wheel-legged Robot: A Comprehensive Review

“…This serves as a compelling foundation for future research, brimming with opportunities for exploration and innovation. Notably, two critical areas warrant attention: the enhancement of optimization techniques and sensor integration, both of which are instrumental in augmenting the robots' perception capabilities [42], [223], [227]- [229]. Furthermore, the exploration of machine learning and adaptive control techniques holds promise.…”

“…Optimize force feedback applications for both familiar and unfamiliar environments. Hybrid Learning and Model-Based Controller [223].…”

“…Validate the L1 adaptive controller in real-world scenarios through testing and validation. Compensation for Uncertainties and Disturbances [223].…”