Whole Body Model Predictive Control

Whole-body Model Predictive Control (MPC) aims to generate complex, dynamic movements for robots with many degrees of freedom by optimizing control actions over a prediction horizon. Current research focuses on improving computational efficiency through techniques like distributed optimization, hierarchical learning, and tailored solution accuracy, often employing algorithms such as the Alternating Direction Method of Multipliers (ADMM) and iterative Linear Quadratic Regulators (iLQR). These advancements enable real-time control of increasingly complex robots, from humanoid and quadrupedal platforms to those with articulated arms, facilitating tasks ranging from locomotion in challenging terrains to agile manipulation and object catching. The resulting improvements in robustness and performance have significant implications for robotics research and applications in areas like industrial automation and disaster response.