Papers
15
Total Citations
144
H-Index
7
About
Josef Pauli’s research lies at the intersection of computer vision, robotics, and machine learning, with a focus on enabling robots to perceive, learn from, and interact with their environments. His pioneering work on learning-based robot vision established foundational methods for object recognition and grasping, as demonstrated in his highly cited 2001 monograph *Learning-Based Robot Vision* (28 citations) and related papers on learning to recognize and grasp objects (22 and 16 citations). Pauli advanced vision-based integrated systems for object inspection and handling (19 citations) and developed servoing mechanisms for precision tasks like peg-in-hole assembly (12 citations). A notable contribution is his early exploration of Programming by Demonstration (PbD), where robots learn trajectories from only a few demonstrated positions—a concept that remains influential in modern robot learning. Later work extended into adaptive robot companions for elderly users, reinforcement learning in real environments using RBF networks, and real-time 3D reconstruction and tracking of moving objects (KinFu MOT). Pauli’s research consistently bridges theoretical learning algorithms with practical robotic applications, making him a key figure in the development of perceptive, adaptable robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Learning-Based Robot Vision28 citations · 2001
- 2Learning to Recognize and Grasp Objects22 citations · 1998
- 3Vision-based integrated system for object inspection and handling19 citations · 2001
- 4Learning to Recognize and Grasp Objects16 citations · 1998
- 5Servoing Mechanisms for Peg-In-Hole Assembly Operations12 citations · 2001
- 6VISION BASED LEARNING OF GRIPPER TRAJECTORIES FOR A ROBOT ARM.9 citations · 1997
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- 10KinFu MOT: KinectFusion with Moving Objects Tracking5 citations · 2015