Advancing Humanoid Robotics: A Decade of Progress in Whole-Body Control
Table of Contents
The field of humanoid robotics is rapidly evolving,driven by advancements in control algorithms that allow these complex machines to navigate and interact with the world more effectively. Recent research, spanning from the early 2000s to 2023, demonstrates a consistent focus on developing robust and efficient whole-body control frameworks, crucial for achieving stable locomotion and versatile manipulation in humanoid robots.
The Foundations of Whole-Body Control
Early work laid the groundwork for modern approaches.In 2000, Bruyninckx and Khatib introduced Gauss’ principle for controlling redundant and constrained manipulators, a concept that heavily influenced subsequent developments. This principle, alongside the work of J. Park and O. Khatib in 2006 on a contact consistent control framework, established the importance of managing contact forces for stable humanoid movement. Sentis and khatib,also in 2006,further refined this concept with a whole-body control framework specifically designed for humanoids operating in human environments,highlighting the need for robots to function safely and effectively alongside people.
Model Predictive Control Takes Center Stage
A significant trend in the past two decades has been the adoption of Model Predictive control (MPC). This advanced technique allows robots to anticipate future states and optimize control actions over a defined time horizon. Koenemann et al. (2015) demonstrated the submission of whole-body MPC to the HRP-2 humanoid,showcasing its ability to generate complex movements. Neunert et al. (2018) extended this approach to quadruped robots, utilizing nonlinear MPC through contacts to achieve dynamic locomotion on challenging terrain. Katayama, Murooka, and Tazaki (2023) provided a comprehensive overview of MPC models and algorithms for both legged and humanoid robots, solidifying its position as a leading control strategy.
Optimizing for Robustness and Adaptability
on robust balance optimization control for humanoid robots, especially when interacting with multiple contact points. Jung et al.(2017) analyzed position tracking in torque control, considering the effects of joint elasticity and time delay. Herzog et al. (2016) introduced momentum control with hierarchical inverse dynamics, improving the coordination of humanoid movements. De Lasa and Hertzmann (2009, 2010) pioneered prioritized optimization and feature-based locomotion controllers, offering flexible and intuitive ways to specify robot behavior. Mesesan et al. (2019) demonstrated dynamic walking on compliant and uneven terrain using a combination of techniques, including the Zero Moment Point (ZMP) concept and passivity-based whole-body control.
Recent Innovations and Future Directions
Recent advancements continue to push the boundaries of humanoid robotics. Jeon et al. (2022) showcased online optimal landing control for the MIT Mini Cheetah, a quadrupedal robot.Lee et al. (2021, 2022) explored whole-body control frameworks based on the operational space formulation, improving the efficiency and robustness of control algorithms. Raiola et al. (2020) presented a simple yet effective framework for quadruped locomotion. Klemm et al. (2020) combined LQR assistance with whole-body control for a wheeled bipedal robot. ramuzat et al. (2021) compared position and torque control schemes on the Talos humanoid robot. Sathya et al.(2021) developed a weighted method for resolving hierarchical task specifications. Ahn et al. (2023) detailed a dual-channel EtherCAT control system for the 33-DOF humanoid robot TOCABI.
The ongoing research, as evidenced by these publications, demonstrates a clear trajectory towards more adaptable, robust, and efficient humanoid robots capable of operating seamlessly in complex, real-world environments. The convergence of advanced control algorithms, powerful optimization techniques, and increasingly elegant hardware promises to unlock the full potential of these remarkable machines.
