103 citations · 328 across the 79 of their papers we have counts for
11 papers · 2 filters
Hoi! - A Multimodal Dataset for Force-Grounded, Cross-View Articulated Manipulation
Tim Engelbracht, René Zurbrügg, Matteo Wohlrapp +5
We present a dataset for force-grounded, cross-view articulated manipulation that couples what is seen with what is done and what is felt during real human interaction. The dataset…
Preference-Based Long-Horizon Robotic Stacking with Multimodal Large Language Models
Wanming Yu, Adrian Röfer, Abhinav Valada +1
Pretrained large language models (LLMs) can work as high-level robotic planners by reasoning over abstract task descriptions and natural language instructions, etc. However, they h…
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,…
Articulated Object Estimation in the Wild
Abdelrhman Werby, Martin Büchner, Adrian Röfer +3
Understanding the 3D motion of articulated objects is essential in robotic scene understanding, mobile manipulation, and motion planning. Prior methods for articulation estimation…
DiWA: Diffusion Policy Adaptation with World Models
Akshay L Chandra, Iman Nematollahi, Chenguang Huang +3
Fine-tuning diffusion policies with reinforcement learning (RL) presents significant challenges. The long denoising sequence for each action prediction impedes effective reward pro…
Designing for Difference: How Human Characteristics Shape Perceptions of Collaborative Robots
Sabrina Livanec, Laura Londoño, Michael Gorki +3
The development of assistive robots for social collaboration raises critical questions about responsible and inclusive design, especially when interacting with individuals from pro…
cVLA: Towards Efficient Camera-Space VLAs
Max Argus, Jelena Bratulic, Houman Masnavi +4
Vision-Language-Action (VLA) models offer a compelling framework for tackling complex robotic manipulation tasks, but they are often expensive to train. In this paper, we propose a…
MORE: Mobile Manipulation Rearrangement Through Grounded Language Reasoning
Mohammad Mohammadi, Daniel Honerkamp, Martin Büchner +5
Autonomous long-horizon mobile manipulation encompasses a multitude of challenges, including scene dynamics, unexplored areas, and error recovery. Recent works have leveraged found…
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…
Robotic Task Ambiguity Resolution via Natural Language Interaction
Eugenio Chisari, Jan Ole von Hartz, Fabien Despinoy +1
Language-conditioned policies have recently gained substantial adoption in robotics as they allow users to specify tasks using natural language, making them highly versatile. While…
Evidential Uncertainty Estimation for Multi-Modal Trajectory Prediction
Sajad Marvi, Christoph Rist, Julian Schmidt +2
Accurate trajectory prediction is crucial for autonomous driving, yet uncertainty in agent behavior and perception noise makes it inherently challenging. While multi-modal trajecto…