1 code implementation • 28 Dec 2020 • Alina Kloss, Georg Martius, Jeannette Bohg
In many robotic applications, it is crucial to maintain a belief about the state of a system, which serves as input for planning and decision making and provides feedback during task execution.
no code implementations • 8 Nov 2019 • Alina Kloss, Maria Bauza, Jiajun Wu, Joshua B. Tenenbaum, Alberto Rodriguez, Jeannette Bohg
Planning contact interactions is one of the core challenges of many robotic tasks.
no code implementations • ICLR 2019 • Alina Kloss, Jeannette Bohg
Recursive Bayesian Filtering algorithms address the state estimation problem, but they require a model of the process dynamics and the sensory observations as well as noise estimates that quantify the accuracy of these models.
1 code implementation • 11 Oct 2017 • Alina Kloss, Stefan Schaal, Jeannette Bohg
In this work, we investigate the advantages and limitations of neural network based learning approaches for predicting the effects of actions based on sensory input and show how analytical and learned models can be combined to leverage the best of both worlds.