Papers
34
Total Citations
1,629
H-Index
19
About
Clemens Eppner is a robotics researcher whose work spans robotic grasping, manipulation, pose estimation, and human-robot interaction. Best known for his systematic contributions to understanding how robots can reliably grasp and manipulate objects, Eppner has made foundational advances in exploiting environmental constraints to improve grasping robustness — a concept explored across multiple highly cited works totaling nearly 250 citations alone. His 2023 review of deep learning approaches to grasp synthesis (215 citations) has become an essential reference for researchers entering the field, while his self-supervised framework for 6D object pose estimation (198 citations) addresses the critical challenge of reducing costly data annotation in robot learning. Eppner's practical impact is underscored by his team's winning entry to the Amazon Picking Challenge, which yielded influential lessons on building real-world robotic systems. His work on dexterous manipulation with soft robotic hands and model-based control of humanoids further demonstrates his broad expertise. From early contributions in humanoid museum tour guides to cutting-edge grasp data evaluation, Eppner's career reflects a consistent drive to bridge theoretical rigor with deployable robotic solutions.
Research Focus
Key Achievements
Top Papers
- 1Deep Learning Approaches to Grasp Synthesis: A Review215 citations · 2023
- 2Exploitation of environmental constraints in human and robotic grasping199 citations · 2015
- 3Self-supervised 6D Object Pose Estimation for Robot Manipulation198 citations · 2020
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- 6The humanoid museum tour guide Robotinho103 citations · 2009
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- 8Planning grasp strategies That Exploit Environmental Constraints54 citations · 2015
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