31 citations · 64 across the 16 of their papers we have counts for
5 papers · 2 filters
Distillation for Efficient Multitask Manipulation Policies via Conditional Flow Matching
Shreya Deshmukh, Imen Mahdi, Nick Heppert +1
Advances in generative modeling have recently been extensively employed in robotics for policy learning. In particular, Conditional Flow Matching (CFM) trained with expert demonstr…
Attention from Action, for Action: Emergent Visual Bottlenecks for Policy Learning
Zheyu Zhuang, Ruiyu Wang, Nick Heppert +4
Visual bottlenecks that focus policy inputs on regions of interest (ROIs) can improve data-efficient visuomotor learning by separating where to look from how to act. Many ROI inter…
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.…