Papers
10
Total Citations
100
H-Index
5
About
Markus Schmitz is a robotics and manufacturing researcher whose work bridges advanced fabrication technologies and intelligent automation systems. His primary contributions span two interconnected domains: multidirectional Wire Arc Additive Manufacturing (WAAM) and deep reinforcement learning for robotic control. In the realm of additive manufacturing, Schmitz has made foundational advances by developing novel path-planning and trajectory-planning algorithms specifically designed for multidirectional WAAM processes involving pure object manipulation — an area where existing approaches had previously fallen short. His 2021 paper introducing this robot-centered path-planning algorithm has garnered 37 citations, establishing it as a key reference in the field. Simultaneously, Schmitz has pursued the challenge of intelligent robotic assembly, applying deep reinforcement learning to enable adaptive, force-sensitive manipulation in contact-rich industrial tasks. His work on reward curriculum approaches and simulation-to-real-world transfer reflects a consistent drive to make fully automated assembly practically viable. He has also explored machine learning applications in laser beam welding quality assessment and extended robotic intelligence to less conventional domains, including activity recognition for robotic cooking. With growing contributions to ROS accessibility and web-based robotics infrastructure, Schmitz continues to broaden the reach and applicability of intelligent robotic systems across both research and industry.
Research Focus
Key Achievements
Top Papers
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- 9Robotik 4.02 citations · 2020
- 10ROS2WASM: Bringing the Robot Operating System to the Web1 citations · 2025