Papers
5
Total Citations
48
H-Index
4
About
Marco Ojer is a leading researcher at the intersection of robotics, computer graphics, and smart manufacturing, whose work is central to the realization of Industry 4.0 and Operator 4.0 paradigms. His key contributions lie in developing visual computing and physically-based simulation as enabling technologies for non-disruptive, interactive solutions in production environments—a framework that has garnered 25 citations. Ojer has also made significant strides in robotic manipulation, notably through a hierarchical reinforcement learning approach for 4-finger grippers, and by creating PickingDK, a standardized, plugin-based industrial bin-picking framework that integrates diverse sensors, robots, and open-source tools. His recent work on edge architectures for flexible manufacturing lines and high-accuracy hybrid kinematic modeling for serial manipulators further demonstrates his commitment to bridging operational technology (OT) and information technology (IT) domains. With a citation count approaching 50 across his most-cited works, Ojer’s research is not only advancing the theoretical foundations of automation but also providing practical, deployable solutions that are shaping the future of intelligent, flexible manufacturing systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Reinforcement Learning for 4-Finger-Gripper Manipulation8 citations · 2018
- 3PickingDK: A Framework for Industrial Bin-Picking Applications6 citations · 2022
- 4
- 5High accuracy hybrid kinematic modeling for serial robotic manipulators4 citations · 2024