62 citations · 148 across the 29 of their papers we have counts for
8 papers · 1 filter
Task-Driven Co-Design of Mobile Manipulators
Raphael Schneider, Daniel Honerkamp, Tim Welschehold +1
Recent interest in mobile manipulation has resulted in a wide range of new robot designs. A large family of these designs focuses on modular platforms that combine existing mobile…
Learning Robotic Manipulation Policies from Point Clouds with Conditional Flow Matching
Eugenio Chisari, Nick Heppert, Max Argus +3
Learning from expert demonstrations is a promising approach for training robotic manipulation policies from limited data. However, imitation learning algorithms require a number of…
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…
Perception Matters: Enhancing Embodied AI with Uncertainty-Aware Semantic Segmentation
Sai Prasanna, Daniel Honerkamp, Kshitij Sirohi +3
Embodied AI has made significant progress acting in unexplored environments. However, tasks such as object search have largely focused on efficient policy learning. In this work, w…
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…
DITTO: Demonstration Imitation by Trajectory Transformation
Nick Heppert, Max Argus, Tim Welschehold +2
Teaching robots new skills quickly and conveniently is crucial for the broader adoption of robotic systems. In this work, we address the problem of one-shot imitation from a single…
Bayesian Optimization for Sample-Efficient Policy Improvement in Robotic Manipulation
Adrian Röfer, Iman Nematollahi, Tim Welschehold +2
Sample efficient learning of manipulation skills poses a major challenge in robotics. While recent approaches demonstrate impressive advances in the type of task that can be addres…
Language-Grounded Dynamic Scene Graphs for Interactive Object Search with Mobile Manipulation
Daniel Honerkamp, Martin Büchner, Fabien Despinoy +2
To fully leverage the capabilities of mobile manipulation robots, it is imperative that they are able to autonomously execute long-horizon tasks in large unexplored environments. W…