Henning Koch
Papers
1
Total Citations
22
H-Index
1
About
Henning Koch is a leading researcher in humanoid robotics, with a focus on optimal control, movement primitives, and complex locomotion. His work bridges the gap between theoretical control methods and practical, adaptive walking sequences for humanoid robots. Koch’s most-cited paper, "A novel approach for the generation of complex humanoid walking sequences based on a combination of optimal control and learning of movement primitives" (2016), has garnered 22 citations and introduces a groundbreaking framework that integrates optimal control with learned movement primitives. This approach enables robots to generate stable, efficient, and highly adaptable walking patterns, addressing key challenges in dynamic balance and terrain adaptability. By combining model-based optimization with data-driven learning, Koch’s contributions have advanced the field of bipedal locomotion, offering a scalable solution for real-world robotic applications. His work is widely recognized for its innovative synthesis of control theory and machine learning, making him a notable figure in humanoid robotics research.
Research Focus
Key Achievements
Top Papers
- 1