Johannes Schilp

About

Johannes Schilp is a leading researcher at the intersection of robotics, manufacturing, and cyber-physical systems, with a focus on making industrial production more flexible and intelligent. His work centers on enabling collaborative robots to learn from human demonstration and natural language, a key step toward automating small-batch, customized manufacturing. His 2022 paper on robot learning from multi-modal demonstration and natural language instruction (9 citations) addresses the critical challenge of frequent robot re-programming in small and medium enterprises. Schilp also made early contributions to microassembly, demonstrating how intelligent vision and smart sensors can achieve high-accuracy assembly for microsystems (2003, 4 citations). More recently, he has advanced production planning for cyber-physical systems, modeling the skills of robots and humans to enable flexible task allocation (2020, 4 citations). His 2025 work on the Asset Administration Shell (AAS) for digital twins of robot systems further underscores his commitment to Industry 4.0 interoperability standards. With a career spanning foundational microassembly to cutting-edge robot learning and digital twins, Schilp’s research is shaping the future of adaptive, human-robot collaborative manufacturing.

Research Focus

Key Achievements

Top Papers

1. 1
   System of Robot Learning from Multi-Modal Demonstration and Natural Language Instruction

   9 citations · 2022
2. 2
   High-accuracy microassembly by intelligent vision systems and smart sensor integration

   4 citations · 2003
3. 3
   Production planning for collaborating resources in cyber-physical production systems

   4 citations · 2020
4. 4
   Einsatz der AAS zur Abbildung eines Robotersystems/Application of the asset administration shell for mapping a modular and connected robot system – System architecture of a digital twin for a robot system

   1 citations · 2025

Key Collaborators

Contact & Links