31 citations · 64 across the 16 of their papers we have counts for
16 papers
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.…
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…
Neural Fields in Robotics: A Survey
Muhammad Zubair Irshad, Mauro Comi, Yen-Chen Lin +5
Neural Fields have emerged as a transformative approach for 3D scene representation in computer vision and robotics, enabling accurate inference of geometry, 3D semantics, and dyna…
Learning Robotic Manipulation Policies from Point Clouds with Conditional Flow Matching
Eugenio Chisari, Nick Heppert, Max Argus +3
Learning from expert demonstrations is a promising approach for training robotic manipulation policies from limited data. However, imitation learning algorithms require a number of…
Imagine2touch: Predictive Tactile Sensing for Robotic Manipulation using Efficient Low-Dimensional Signals
Abdallah Ayad, Adrian Röfer, Nick Heppert +1
Humans seemingly incorporate potential touch signals in their perception. Our goal is to equip robots with a similar capability, which we term Imagine2touch. Imagine2touch aims to…
CenterArt: Joint Shape Reconstruction and 6-DoF Grasp Estimation of Articulated Objects
Sassan Mokhtar, Eugenio Chisari, Nick Heppert +1
Precisely grasping and reconstructing articulated objects is key to enabling general robotic manipulation. In this paper, we propose CenterArt, a novel approach for simultaneous 3D…
DITTO: Demonstration Imitation by Trajectory Transformation
Nick Heppert, Max Argus, Tim Welschehold +2
Teaching robots new skills quickly and conveniently is crucial for the broader adoption of robotic systems. In this work, we address the problem of one-shot imitation from a single…
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…
CenterGrasp: Object-Aware Implicit Representation Learning for Simultaneous Shape Reconstruction and 6-DoF Grasp Estimation
Eugenio Chisari, Nick Heppert, Tim Welschehold +2
Reliable object grasping is a crucial capability for autonomous robots. However, many existing grasping approaches focus on general clutter removal without explicitly modeling obje…
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…
CARTO: Category and Joint Agnostic Reconstruction of ARTiculated Objects
Nick Heppert, Muhammad Zubair Irshad, Sergey Zakharov +5
We present CARTO, a novel approach for reconstructing multiple articulated objects from a single stereo RGB observation. We use implicit object-centric representations and learn a…
Category-Independent Articulated Object Tracking with Factor Graphs
Nick Heppert, Toki Migimatsu, Brent Yi +2
Robots deployed in human-centric environments may need to manipulate a diverse range of articulated objects, such as doors, dishwashers, and cabinets. Articulated objects often com…