Minimum angular acceleration control of articulated body dynamics

As robots find applications in daily life conditions it becomes important to develop controllers that generate energy efficient movements by restricting variability and utilizing high gains only when necessary. Here we present a computationally light and energy efficient approach (AAC) that combines an anticipatory open-loop controller and a variable gain closed loop controller. The approach is grounded in the theory of stochastic optimal control and feedback linearization. As such it links two important approaches to robot control: (1) the family of Computed Torque Controllers (CTC) that are grounded on feedback linearization and classic feedback control, and (2) a more recent family of controllers that aim at finding approximately optimal trade-offs between task performance and energy consumption. Here we show that AAC controllers are highly energy efficient, when compared to CTC, and exhibit some key properties of human motion.