Uchenna Emeoha Ogenyi
Papers
6
Total Citations
159
H-Index
3
About
Uchenna Emeoha Ogenyi is a robotics researcher whose work centers on physical human-robot collaboration (pHRC), learning from demonstration, and multimodal sensing. His most impactful contribution is the comprehensive 2019 survey "Physical Human–Robot Collaboration: Robotic Systems, Learning Methods, Collaborative Strategies, Sensors, and Actuators," which has garnered 125 citations and serves as a foundational reference for researchers designing safe, intuitive robotic systems for shared workspaces. Ogenyi has also advanced assistive technology through a two-stream CNN framework for American Sign Language recognition (21 citations), fusing multimodal data to improve human-robot communication. His work on robot learning from human demonstration (6 citations) and adaptive collision-free skill acquisition (2 citations) addresses core challenges in making industrial robots more responsive and safer in dynamic environments. More recently, he has explored IoT-integrated smart systems, including a gesture-controlled radiator for enhanced thermal management (2024). Ogenyi’s research consistently bridges the gap between human intent and robotic action, with a strong emphasis on real-time adaptability and intuitive interfaces—critical for next-generation collaborative automation.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3An Intuitive Robot Learning from Human Demonstration6 citations · 2018
- 4
- 5Adaptive Collision-Free Reaching Skill Learning from Demonstration2 citations · 2020
- 6