Nick Heppert
Papers
5
Total Citations
54
H-Index
4
About
Nick Heppert is a robotics researcher whose work focuses on enabling robots to perceive, grasp, and manipulate objects in human-centric environments with greater autonomy and adaptability. His key research areas include 6-DoF grasp estimation, articulated object tracking, and one-shot imitation learning. Heppert’s major contributions include **CenterGrasp**, a novel framework that combines object-aware implicit representation learning for simultaneous shape reconstruction and grasp estimation, achieving 19 citations. He also developed **category-independent articulated object tracking** using factor graphs, which allows robots to handle unexpected articulation mechanisms without relying on categorical priors, cited 16 times. His work **DITTO** addresses one-shot imitation from a single human RGB-D video demonstration, enabling quick skill transfer to robots (12 citations). Additionally, **AO-Grasp** generates 6-DoF grasps specifically for articulated objects like cabinets and appliances, enhancing robotic interaction in everyday settings. Heppert’s research has been recognized for its practical impact, with a combined citation count of over 50, and his methods are paving the way for more versatile and intuitive robotic systems in dynamic, unstructured environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2Category-Independent Articulated Object Tracking with Factor Graphs16 citations · 2022
- 3DITTO: Demonstration Imitation by Trajectory Transformation12 citations · 2024
- 4AO-Grasp: Articulated Object Grasp Generation5 citations · 2024
- 5Category-Independent Articulated Object Tracking with Factor Graphs2 citations · 2022