Max Robotics — Global Service Robot Directory + US Certification

About

Joris Sijs is a leading researcher in human-machine teaming and intelligent autonomous systems, with a career dedicated to making robots more capable, intuitive, and safe for real-world interaction. His work spans three core areas: assistive robotics, human-robot delegation, and semantic world modeling. In his early career, Sijs advanced vision-based control for rehabilitation robots like the Manus and ARM manipulators, enabling severely motor-impaired users to perform daily tasks more easily through combined position and force control (over 48 combined citations). More recently, he has pioneered frameworks for meaningful human control over autonomous vehicles, including unmanned aerial and ground systems, where his work on delegation in human-machine teaming (2021) has become a key reference. Sijs is also at the forefront of semantic understanding for robots, developing hypergraph-based knowledge bases and incremental learning of affordances using Markov Logic Networks—allowing robots to model complex, real-world environments and adapt their understanding over time. His notable contributions include extending robot knowledge structures to support both navigation and task execution in indoor settings, bridging the gap between perception and action. With a growing citation impact and a clear trajectory toward more intelligent, context-aware autonomy, Sijs’s research is shaping the future of collaborative robotics.