11 citations · 11 across the 5 of their papers we have counts for
5 papers
One Hand Watches The Other: Dynamic Multi-Agent Cooperation for Sample-Efficient Bimanual Manipulation in Dynamic Environments
Jan Ole von Hartz, Abhinav Valada, Joschka Boedecker
Multi-stream robot manipulation policies achieve unparalleled sample efficiency and generalization by modeling actions relative to environmental reference frames. However, existing…
The Art of Imitation: Learning Long-Horizon Manipulation Tasks from Few Demonstrations
Jan Ole von Hartz, Tim Welschehold, Abhinav Valada +1
Task Parametrized Gaussian Mixture Models (TP-GMM) are a sample-efficient method for learning object-centric robot manipulation tasks. However, there are several open challenges to…
The Treachery of Images: Bayesian Scene Keypoints for Deep Policy Learning in Robotic Manipulation
Jan Ole von Hartz, Eugenio Chisari, Tim Welschehold +3
In policy learning for robotic manipulation, sample efficiency is of paramount importance. Thus, learning and extracting more compact representations from camera observations is a…
Safe Imitation Learning of Nonlinear Model Predictive Control for Flexible Robots
Shamil Mamedov, Rudolf Reiter, Seyed Mahdi Basiri Azad +4
Flexible robots may overcome some of the industry's major challenges, such as enabling intrinsically safe human-robot collaboration and achieving a higher payload-to-mass ratio. Ho…
Adaptively Calibrated Critic Estimates for Deep Reinforcement Learning
Nicolai Dorka, Tim Welschehold, Joschka Boedecker +1
Accurate value estimates are important for off-policy reinforcement learning. Algorithms based on temporal difference learning typically are prone to an over- or underestimation bi…