8 papers · 1 filter
AnchorD: Metric Grounding of Monocular Depth Using Factor Graphs
Simon Dorer, Martin Büchner, Nick Heppert +1
Dense and accurate depth estimation is essential for robotic manipulation, grasping, and navigation, yet currently available depth sensors are prone to errors on transparent, specu…
SparTa: Sparse Graphical Task Models from a Handful of Demonstrations
Adrian Röfer, Nick Heppert, Abhinav Valada
Learning long-horizon manipulation tasks efficiently is a central challenge in robot learning from demonstration. Unlike recent endeavors that focus on directly learning the task i…
Scaling Single Human Demonstrations for Imitation Learning using Generative Foundational Models
Nick Heppert, Minh Quang Nguyen, Abhinav Valada
Imitation learning is a popular paradigm to teach robots new tasks, but collecting robot demonstrations through teleoperation or kinesthetic teaching is tedious and time-consuming.…
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…
AO-Grasp: Articulated Object Grasp Generation
Carlota Parés Morlans, Claire Chen, Yijia Weng +6
We introduce AO-Grasp, a grasp proposal method that generates 6 DoF grasps that enable robots to interact with articulated objects, such as opening and closing cabinets and applian…
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…