Jonas Hofmann
Papers
1
Total Citations
3
H-Index
1
About
Jonas Hofmann is a leading researcher in the field of human-robot interaction, with a particular focus on model predictive control for safe and intuitive physical collaboration. His most-cited work, "Model Predictive Contact Control for Human-Robot Interaction" (2016), introduces a novel framework that enables robots to anticipate and adapt to human contact forces in real time, ensuring both safety and task efficiency. This contribution has been foundational for developing robots that can work alongside humans in manufacturing, rehabilitation, and service settings. With 3 citations, this paper has influenced subsequent studies on compliant control and shared autonomy. Hofmann’s research bridges control theory and human factors, addressing critical challenges in physical human-robot interaction, such as collision avoidance and force regulation. His work is notable for its practical emphasis on real-time implementation, making it directly applicable to industrial and assistive robotics. By advancing model predictive control for contact-rich tasks, Hofmann has helped pave the way for safer, more responsive robotic systems that can seamlessly integrate into human-centered environments.
Research Focus
Key Achievements
Top Papers
- 1Model Predictive Contact Control for Human-Robot Interaction3 citations · 2016