title: Real Time Probabilistic Models for Robot Trajectories
Creators: name: Gomez Gonzalez, Sebastian
Publisher: name: UNSPECIFIED
Year: 2020

description: Robot learning has the potential to give robotic systems the ability to perform multiple tasks and solve difficult tasks in dynamic environments. Probabilistic approaches to robot learning have several properties interesting for robotic applications such as providing uncertainty estimates and likelihood evaluations, useful for decision making and finding atypical environment states where acting might be dangerous for the robot. There are also some typical challenges that robot learning in general and specially probabilistic approaches face for robotics. Real time robot applications such as robot table tennis place strict latency requirements for prediction, likelihood evaluations or other important operators. The amount of data available for learning in robotic applications is also typically not very large, increasing the risks of overfitting specially for probabilistic approaches that usually have more parameters than deterministic methods for the same predictive accuracy. Finally, for certain applications with complex sensors such as computer vision systems it is important to have robot learning methods capable of operating with missing observations and outliers. In this thesis, we use robot table tennis as an example of a challenging application to propose or extend probabilistic learning approaches for trajectory representations. We place special focus on evaluating the latency of the real time critical operators, trying to ensure safety of the robot to unexpected environment states, operating with missing observations or outliers, and learning with relatively small training sets. Although table tennis is our inspiring application, we propose operators that can be used for other robot applications, trying to keep the table tennis specific heuristics to a minimum. First we discuss how to learn a robot policy from demonstrations using Probabilistic Movement Primitives. We propose a learning method to learn a movement primitive from a small set of demonstrations performed by a human expert. We compare the proposed learning method with a least squares based method, showing that the least squares method is a special case of the proposed learning algorithm. We also show experimentally that the proposed learning method does not suffer from the overfitting problems of the least squares method and the table tennis hitting and return rate is superior. We also propose adaptation operators in joint and task space for the learned movement primitives, necessary to react to changes in the robot environment such as different incoming ball trajectories or the location of objects like a grinder and brewing chamber for a coffee preparation task. We also present a vision system for real-time object tracking. We focus on reliability of the estimates produced by the vision system, reducing the number of outliers to a minimum, specially as the number of available cameras increases. We use the proposed vision system to track the table tennis ball for robot table tennis with a speed of 180 frames per second. Finally, we introduce a new method for forecasting the future value of a trajectory given its past observations based on variational auto-encoders. We use the proposed model to predict the trajectory of the ball from previous observations of the ball position. The proposed method has a better accuracy for long term predictions than traditional time series forecasting methods such as recurrent neural networks or using differential equations based of physical models, provided that the spin of the ball is not observed by the vision system.