This paper presents a method to enable a robot using stochastic Model Predictive Control (MPC) to achieve high performance on a repetitive path-following task. In particular, we consider the case where the accuracy of the model for robot dynamics varies significantly over the path-motivated by the fact that the models used in MPC must be computationally efficient, which limits their expressive power. Our approach is based on correcting the cost predicted using a simple learned dynamics model over the MPC horizon. This discourages the controller from taking actions that lead to higher cost than would have been predicted using the dynamics model. In addition, stochastic MPC provides a quantitative measure of safety by limiting the probability of violating state and input constraints over the prediction horizon. Our approach is unique in that it combines both online model learning and cost learning over the prediction horizon and is geared towards operating a robot in changing conditions. We demonstrate our algorithm in simulation and experiment on a ground robot that uses a stereo camera for localization.

Learning approaches have enabled significant performance improvements in robotic control allowing robots to execute motions that were previously impossible. The majority of the work to date, however, assumes that the parts to be learned are static or slowly changing, which limits their applicability in realistic scenarios with rapid changes in the conditions. This paper presents a method to extend an existing single-mode safe learning controller based on Gaussian Process Regression 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 experiences 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 a 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.

For decades, robots have been essential allies alongside humans in controlled industrial environments like heavy manufacturing facilities. However, without the guidance of a trusted human operator to shepherd a robot safely through a wide range of conditions, they have been barred from the complex, ever changing environments that we live in from day to day. Safe learning control has emerged as a promising way to start bridging algorithms based on first principles to complex real-world scenarios by using data to adapt, and improve performance over time. Safe learning methods rely on a good estimate of the robot dynamics and of the bounds on modelling error in order to be effective. Current methods focus on either a single adaptive model, or a fixed, known set of models for the robot dynamics. This limits them to static or slowly changing environments. This paper presents a method using Gaussian Processes in a Dirichlet Process mixture model 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 an arbitrary number of previously visited operating conditions, and to automatically learn a new model when a new and distinct operating condition is encountered. This approach improves the robustness of existing Gaussian Process-based models to large changes in dynamics that do not have to be specified ahead of time.

In this paper, we describe an algorithm, based on the well-known Unscented Quaternion Estimator, to estimate external forces and torques acting on a quadrotor. This formulation uses a non-linear model for the quadrotor dynamics, naturally incorporates process and measurement noise, requires only a few parameters to be tuned manually, and uses singularity-free unit quaternions to represent attitude. We demonstrate in simulation that the proposed algorithm can outperform existing methods. We then highlight how our approach can be used to generate force and torque profiles from experimental data, and how this information can later be used for controller design. Finally, we show how the resulting controllers enable a quadrotor to stay in the wind field of a moving fan.