5 papers
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…
LIME: Learning Intent-aware Camera Motion from Egocentric Video
Boyang Sun, Jiajie Li, Yung-Hsu Yang +6
Autonomous robots often need to move their camera before they can act: to inspect an object, reveal an occluded region, or obtain a view that responds to a user's intent. While vis…
Articulated 3D Scene Graphs for Open-World Mobile Manipulation
Martin Büchner, Adrian Röfer, Tim Engelbracht +5
Semantic scene understanding has enabled robots to reason and perform affordance-driven object interaction. Despite this, real-world manipulation remains fundamentally limited by t…
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…
Spot-On: A Mixed Reality Interface for Multi-Robot Cooperation
Tim Engelbracht, Petar Lukovic, Tjark Behrens +5
Recent progress in mixed reality (MR) and robotics is enabling increasingly sophisticated forms of human-robot collaboration. Building on these developments, we introduce a novel M…