Automatic LQR Tuning Based on Gaussian Process Global Optimization
This work addresses the challenge of reducing manual tuning effort for controllers in robotics, though it appears incremental as it builds on existing Bayesian optimization methods.
The paper tackles the problem of automatically tuning controller gains for robotic systems by proposing a framework that combines linear optimal control with Bayesian optimization, specifically using Entropy Search to minimize experimental evaluations. Results on a seven-degree-of-freedom robot arm show potential for efficient tuning in two- and four-dimensional problems.
This paper proposes an automatic controller tuning framework based on linear optimal control combined with Bayesian optimization. With this framework, an initial set of controller gains is automatically improved according to a pre-defined performance objective evaluated from experimental data. The underlying Bayesian optimization algorithm is Entropy Search, which represents the latent objective as a Gaussian process and constructs an explicit belief over the location of the objective minimum. This is used to maximize the information gain from each experimental evaluation. Thus, this framework shall yield improved controllers with fewer evaluations compared to alternative approaches. A seven-degree-of-freedom robot arm balancing an inverted pole is used as the experimental demonstrator. Results of a two- and four-dimensional tuning problems highlight the method's potential for automatic controller tuning on robotic platforms.