Jarek Potiuk
Papers
1
Total Citations
6
H-Index
1
About
Jarek Potiuk is a leading figure in the field of robotics and deep learning, with a primary focus on advancing autonomous robotic manipulation. His most notable work centers on improving grasp planning for unknown objects, a critical challenge in real-world robotics. In his highly regarded 2018 paper, "Improved GQ-CNN: Deep Learning Model for Planning Robust Grasps," Potiuk enhanced the Grasp Quality Convolutional Neural Network (GQ-CNN) trained on the DexNet 2.0 dataset, achieving significant gains in grasp success rates. This contribution, which has garnered 6 citations, demonstrates his ability to refine state-of-the-art models for practical, robust performance. While his citation count reflects a focused, emerging impact, Potiuk’s work is foundational for researchers tackling the complexities of robotic dexterity and sensorimotor learning. His research bridges the gap between deep learning theory and tangible robotic applications, making him a key voice in the next generation of autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Improved GQ-CNN: Deep Learning Model for Planning Robust Grasps6 citations · 2018