11 citations · 16 across the 6 of their papers we have counts for
8 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…
MSG: Multi-Stream Generative Policies for Sample-Efficient Robotic Manipulation
Jan Ole von Hartz, Lukas Schweizer, Joschka Boedecker +1
Generative robot policies such as Flow Matching offer flexible, multi-modal policy learning but are sample-inefficient. Although object-centric policies improve sample efficiency,…
The Unreasonable Effectiveness of Discrete-Time Gaussian Process Mixtures for Robot Policy Learning
Jan Ole von Hartz, Adrian Röfer, Joschka Boedecker +1
We present Mixture of Discrete-time Gaussian Processes (MiDiGap), a novel approach for flexible policy representation and imitation learning in robot manipulation. MiDiGap enables…
Whole-Body Teleoperation for Mobile Manipulation at Zero Added Cost
Daniel Honerkamp, Harsh Mahesheka, Jan Ole von Hartz +2
Demonstration data plays a key role in learning complex behaviors and training robotic foundation models. While effective control interfaces exist for static manipulators, data col…
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…
Self-Supervised Learning of Multi-Object Keypoints for Robotic Manipulation
Jan Ole von Hartz, Eugenio Chisari, Tim Welschehold +1
In recent years, policy learning methods using either reinforcement or imitation have made significant progress. However, both techniques still suffer from being computationally ex…
MDP Playground: An Analysis and Debug Testbed for Reinforcement Learning
Raghu Rajan, Jessica Lizeth Borja Diaz, Suresh Guttikonda +4
We present MDP Playground, a testbed for Reinforcement Learning (RL) agents with dimensions of hardness that can be controlled independently to challenge agents in different ways a…