17 citations · 18 across the 11 of their papers we have counts for
12 papers
MOSAIC: Mode- and Support-Aware Inference-Time Control of Robot Diffusion Models
Sabrina Bodmer, Rahel Rickenbach, René Zurbrügg +2
Generative policies such as diffusion models provide expressive data-driven priors for robot behavior, yet the situations a robot faces at execution time rarely match its training…
ForceTwin: Physics-informed Digital Twins for Robotic Manipulation from Instrumented Human Interaction
Tim Engelbracht, René Zurbrügg, Mayank Mittal +4
Manipulating objects requires understanding not only their motion, but also the physical properties that determine it. For articulated objects, these include inertia, friction, and…
ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients
Yixiao Li, Tifanny Portela, Jordis Herrmann +2
Neural Motion Planners (NMPs) enable fast reactive motion generation, but adapting them to new environments typically requires recollecting large expert datasets, which is computat…
TactSpace: Learning a Physics-enriched Shared Latent Space for Tactile Sim-to-Real Transfer
Arunim Joarder, Arjun Bhardwaj, René Zurbrügg +6
Tactile sensing provides direct measurements of contact interactions that are essential for robotic manipulation. However, current simulators lack the fidelity to faithfully model…
Dense Force Estimation with an Event-based Optical Tactile Sensor
Agis Politis, René Zurbrügg, Valentina Cavinato
Humans rely on spatially dense, geometry and force-aware tactile feedback at high temporal resolution for dexterous manipulation. While vision-based tactile sensors enable dense fo…
VR-DAgger: Immersive VR for Dexterous Data Collection and Uncertainty-Guided On-Policy Correction
René Zurbrügg, Tifanny Portela, Arjun Bhardwaj +3
Learning from demonstrations is effective for robotic manipulation, but collecting sufficient task-specific data remains a major bottleneck. Under distribution shift, small errors…
NARRATE: Versatile Language Architecture for Optimal Control in Robotics
Seif Ismail, Antonio Arbues, Ryan Cotterell +2
The impressive capabilities of Large Language Models (LLMs) have led to various efforts to enable robots to be controlled through natural language instructions, opening exciting po…
ICGNet: A Unified Approach for Instance-Centric Grasping
René Zurbrügg, Yifan Liu, Francis Engelmann +4
Accurate grasping is the key to several robotic tasks including assembly and household robotics. Executing a successful grasp in a cluttered environment requires multiple levels of…
Chronos and CRS: Design of a miniature car-like robot and a software framework for single and multi-agent robotics and control
Andrea Carron, Sabrina Bodmer, Lukas Vogel +6
From both an educational and research point of view, experiments on hardware are a key aspect of robotics and control. In the last decade, many open-source hardware and software fr…
Embodied Active Domain Adaptation for Semantic Segmentation via Informative Path Planning
René Zurbrügg, Hermann Blum, Cesar Cadena +2
This work presents an embodied agent that can adapt its semantic segmentation network to new indoor environments in a fully autonomous way. Because semantic segmentation networks f…
Self-Improving Semantic Perception for Indoor Localisation
Hermann Blum, Francesco Milano, René Zurbrügg +3
We propose a novel robotic system that can improve its perception during deployment. Contrary to the established approach of learning semantics from large datasets and deploying fi…
ACDC: The Adverse Conditions Dataset with Correspondences for Robust Semantic Driving Scene Perception
Christos Sakaridis, Haoran Wang, Ke Li +6
Level-5 driving automation requires a robust visual perception system that can parse input images under any condition. However, existing driving datasets for dense semantic percept…