Chris Manzie
Papers
14
Total Citations
88
H-Index
5
About
Chris Manzie is a researcher whose work spans robotics, control systems, and human-machine interfaces, with particular emphasis on dexterous manipulation, prosthetics, and multi-robot systems. His most recognized contribution is the development of tactile-based blind grasping — a framework enabling robotic hands to manipulate unknown objects using only onboard sensors, without prior object knowledge. This work, which has garnered 30 citations since 2018, addressed a long-standing challenge in both industrial robotics and prosthetic hand design, with follow-up studies extending the approach to trajectory tracking and disturbance rejection. Manzie has also made meaningful contributions to prosthetic personalization, exploring how inherent human motor behavior can be exploited to autonomously tune human-prosthetic interfaces in real time. His research extends into multi-robot task allocation, examining the sensitivity of auction algorithms for efficient coordination among robot teams, and into human-swarm trust dynamics. Complementing this engineering focus, Manzie has investigated occupational health in physically demanding industries, including a study on lumbar injury prevention in sheep shearing. His breadth of contributions — from nonlinear model predictive control theory to applied prosthetics — reflects a researcher comfortable bridging rigorous mathematical foundations with real-world human-centered challenges.
Research Focus
Key Achievements
Top Papers
- 1
- 2Auction algorithm sensitivity for multi-robot task allocation11 citations · 2023
- 3
- 4
- 5
- 6
- 7Complexity minimisation of suboptimal MPC without terminal constraints3 citations · 2020
- 8Individual and Team Trust Preferences for Robotic Swarm Behaviors3 citations · 2022
- 9
- 10