10 papers
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…
ArtiTwinSplat: Interactable Digital Twin Reconstruction via Gaussian Splatting from RGB-D videos
Pranjal Mishra, René Zurbrügg, Max Wilder-Smith +4
Deploying robots in unstructured real-world environments needs accurate, interactive models of the objects. Constructing these models at scale remains a critical bottleneck for rob…
Grounding Generative Policies in Physics: Optimization-Guided Diffusion for Robot Control
Sabrina Bodmer, René Zurbrügg, Tifanny Portela +5
Diffusion models sample effectively from high-dimensional, multimodal distributions, but their outputs may violate deployment constraints. For task-space robot policies, generated…
Geometric Action Model for Robot Policy Learning
Jisang Han, Seonghu Jeon, Jaewoo Jung +7
Generalist robot policies must follow user instructions while reasoning about how objects, cameras, and robot actions interact in the 3D physical world. Recent vision-language-acti…
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…
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…