103 citations · 328 across the 79 of their papers we have counts for
19 papers · 2 filters
NeRF and Gaussian Splatting SLAM in the Wild
Fabian Schmidt, Markus Enzweiler, Abhinav Valada
Navigating outdoor environments with visual Simultaneous Localization and Mapping (SLAM) systems poses significant challenges due to dynamic scenes, lighting variations, and season…
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…
ROVER: A Multi-Season Dataset for Visual SLAM
Fabian Schmidt, Julian Daubermann, Marcel Mitschke +4
Robust SLAM is a crucial enabler for autonomous navigation in natural, semi-structured environments such as parks and gardens. However, these environments present unique challenges…
Efficient Learning of Object Placement with Intra-Category Transfer
Adrian Röfer, Russell Buchanan, Max Argus +2
Efficient learning from demonstration for long-horizon tasks remains an open challenge in robotics. While significant effort has been directed toward learning trajectories, a recen…
Neural Fields in Robotics: A Survey
Muhammad Zubair Irshad, Mauro Comi, Yen-Chen Lin +5
Neural Fields have emerged as a transformative approach for 3D scene representation in computer vision and robotics, enabling accurate inference of geometry, 3D semantics, and dyna…
Stochasticity in Motion: An Information-Theoretic Approach to Trajectory Prediction
Aron Distelzweig, Andreas Look, Eitan Kosman +3
In autonomous driving, accurate motion prediction is crucial for safe and efficient motion planning. To ensure safety, planners require reliable uncertainty estimates of the predic…
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…
Visual-Inertial SLAM for Unstructured Outdoor Environments: Benchmarking the Benefits and Computational Costs of Loop Closing
Fabian Schmidt, Constantin Blessing, Markus Enzweiler +1
Simultaneous Localization and Mapping (SLAM) is essential for mobile robotics, enabling autonomous navigation in dynamic, unstructured outdoor environments without relying on exter…
Learning Lane Graphs from Aerial Imagery Using Transformers
Martin Büchner, Simon Dorer, Abhinav Valada
The robust and safe operation of automated vehicles underscores the critical need for detailed and accurate topological maps. At the heart of this requirement is the construction o…
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…
Imagine2touch: Predictive Tactile Sensing for Robotic Manipulation using Efficient Low-Dimensional Signals
Abdallah Ayad, Adrian Röfer, Nick Heppert +1
Humans seemingly incorporate potential touch signals in their perception. Our goal is to equip robots with a similar capability, which we term Imagine2touch. Imagine2touch aims to…
CenterArt: Joint Shape Reconstruction and 6-DoF Grasp Estimation of Articulated Objects
Sassan Mokhtar, Eugenio Chisari, Nick Heppert +1
Precisely grasping and reconstructing articulated objects is key to enabling general robotic manipulation. In this paper, we propose CenterArt, a novel approach for simultaneous 3D…
Hierarchical Open-Vocabulary 3D Scene Graphs for Language-Grounded Robot Navigation
Abdelrhman Werby, Chenguang Huang, Martin Büchner +2
Recent open-vocabulary robot mapping methods enrich dense geometric maps with pre-trained visual-language features. While these maps allow for the prediction of point-wise saliency…
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…
PseudoTouch: Efficiently Imaging the Surface Feel of Objects for Robotic Manipulation
Adrian Röfer, Nick Heppert, Abdallah Ayad +2
Tactile sensing is vital for human dexterous manipulation, however, it has not been widely used in robotics. Compact, low-cost sensing platforms can facilitate a change, but unlike…
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…