29 citations · 83 across the 18 of their papers we have counts for
10 papers · 1 filter
A Study on Dense and Sparse (Visual) Rewards in Robot Policy Learning
Abdalkarim Mohtasib, Gerhard Neumann, Heriberto Cuayahuitl
Deep Reinforcement Learning (DRL) is a promising approach for teaching robots new behaviour. However, one of its main limitations is the need for carefully hand-coded reward signal…
Navigate-and-Seek: a Robotics Framework for People Localization in Agricultural Environments
Riccardo Polvara, Francesco Del Duchetto, Gerhard Neumann +1
The agricultural domain offers a working environment where many human laborers are nowadays employed to maintain or harvest crops, with huge potential for productivity gains throug…
Learning Riemannian Manifolds for Geodesic Motion Skills
Hadi Beik-Mohammadi, Søren Hauberg, Georgios Arvanitidis +2
For robots to work alongside humans and perform in unstructured environments, they must learn new motion skills and adapt them to unseen situations on the fly. This demands learnin…
Differentiable Robust LQR Layers
Ngo Anh Vien, Gerhard Neumann
This paper proposes a differentiable robust LQR layer for reinforcement learning and imitation learning under model uncertainty and stochastic dynamics. The robust LQR layer can ex…
Residual Feedback Learning for Contact-Rich Manipulation Tasks with Uncertainty
Alireza Ranjbar, Ngo Anh Vien, Hanna Ziesche +2
While classic control theory offers state of the art solutions in many problem scenarios, it is often desired to improve beyond the structure of such solutions and surpass their li…
Action-Conditional Recurrent Kalman Networks For Forward and Inverse Dynamics Learning
Vaisakh Shaj, Philipp Becker, Dieter Buchler +5
Estimating accurate forward and inverse dynamics models is a crucial component of model-based control for sophisticated robots such as robots driven by hydraulics, artificial muscl…