The objective of this research is to develop algorithms that can make robots and simulated characters move like humans. A range of dynamic locomotion tasks including walking, running, getting up and climbing, as well as task variations such as walking backwards, and concurrent tasks such as holding a cup of water while walking, will be studied. The approach is based on optimal control theory. Human movements will be analyzed, and the performance criteria with respect to which they are optimal will be identified. Algorithms that optimize the same performance criteria will then be developed.
Intellectual merit: Movement analysis will be based on a new mathematical framework where inference of performance criteria from observed movements becomes a convex optimization problem. Control synthesis will exploit new algorithms for real-time optimization which are able to plan long movement sequences involving multiple contact events. These algorithms rely on novel formulations of the physics of contact which are more amenable to numerical optimization, as well as a new physics simulator which exploits advances in parallel processing.
Broader impact: This research will change how robots and simulated characters move. Currently many robotic control systems with the appearance of dynamic movements are controlled in open loop, or are designed to execute one specific task. This work will enable robots to express more natural and versatile movements, as well as make robot programming more automated. The resulting controllers will also serve as models for human motor control.
