Experience Recommendation for Long Term Safe Learning-based Model Predictive Control in Changing Operating Conditions
Learning has propelled the cutting edge of performance in robotic control to new heights, allowing robots to operate with high performance in conditions that were previously unimaginable. The majority of the work, however, assumes that the unknown parts are static or slowly changing. This limits them to static or slowly changing environments. However, in the real world, a robot may experience various unknown conditions. This paper presents a method to extend an existing single mode GP-based safe learning controller to learn an increasing number of non-linear models for the robot dynamics. We show that this approach enables a robot to re-use past experience from a large number of previously visited operating conditions, and to safely adapt when a new and distinct operating condition is encountered. This allows the robot to achieve safety and high performance in an large number of operating conditions that do not have to be specified ahead of time. Our approach runs independently from the controller, imposing no additional computation time on the control loop regardless of the number of previous operating conditions considered. We demonstrate the effectiveness of our approach in experiment on a 900 kg ground robot with both physical and artificial changes to its dynamics. All of our experiments are conducted using vision for localization.
03/11/2018 ∙ by Christopher D. McKinnon, et al. ∙ 0 ∙ share
Learn Fast, Forget Slow: Safe Predictive Learning Control for Systems with Unknown, Changing Dynamics Performing Repetitive Tasks
We present a control method for improved repetitive path following for a ground vehicle that is geared towards long-term operation where the operating conditions can change over time and are initially unknown. We use weighted Bayesian Linear Regression to model the unknown actuator dynamics, and show how this simple model is more accurate in both its estimate of the mean behaviour and model uncertainty than Gaussian Process Regression and generalizes to novel operating conditions with little or no tuning. In addition, it allows us to use fast adaptation and long-term learning in one, unified framework, to adapt quickly to new operating conditions and learn repetitive model errors over time. This comes with the added benefit of lower computational cost, longer look-ahead, and easier optimization when the model is used in a robust, Model Predictive controller (MPC). In order to fully capitalize on the long prediction horizons that are possible with this new approach, we use Tube MPC to reduce predicted uncertainty growth. We demonstrate the effectiveness of our approach in experiment on a 900 kg ground robot showing results over 2.7 km of driving with both physical and artificial changes to the robot's dynamics. All of our experiments are conducted using a stereo camera for localization.
10/15/2018 ∙ by Christopher D. McKinnon, et al. ∙ 0 ∙ share
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