6 accepted papers
This paper presents a model-learning method for Stochastic Model Predictive Control (SMPC) that is both accurate and computationally efficient. We assume that the control input affects the robot dynamics through an unknown (but invertable) nonlinear function. By learning this unknown function and it
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 f…
We present a control method for improved repetitive path following for a ground vehicle that is geared toward longterm operation, where the operating conditions can change over time and are initially unknown. We use weighted Bayesian linear regression (wBLR) to model the unknown dynamics, and show h
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…
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,…
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 f…