Papers
70
Total Citations
1,500
H-Index
18
About
Stefanos Nikolaidis is a leading researcher in human-robot collaboration, with a particular focus on mutual adaptation, trust-aware decision making, and collaborative task planning. His work bridges computational modeling and human factors to design robotic systems that work seamlessly alongside people in real-world environments. Nikolaidis is perhaps best known for pioneering human-robot cross-training, a strategy adapted from validated human team training practices in which humans and robots iteratively switch roles to develop shared task plans. This foundational work, accumulating over 300 citations across multiple publications, demonstrated measurable improvements in team performance and convergence. His 2017 paper introducing the Bounded-Memory Adaptation Model (191 citations) formalized mutual adaptation as a probabilistic framework, advancing how robots can dynamically respond to human behavior. A defining thread in his research is the integration of trust into robot decision-making. Through partially observable Markov decision process (POMDP) models learned from data, his work on trust-aware planning (collectively over 197 citations) enables robots to reason about human confidence in autonomous systems — a critical factor for real-world adoption. With applications spanning aerospace manufacturing and assistive robotics, and contributions to intent inference and action prediction, Nikolaidis has helped establish the computational and empirical foundations of effective, human-centered human-robot teaming.
Research Focus
Key Achievements
Top Papers
- 1Human-robot mutual adaptation in collaborative tasks: Models and experiments191 citations · 2017
- 2Trust-Aware Decision Making for Human-Robot Collaboration137 citations · 2020
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- 7Planning with Trust for Human-Robot Collaboration60 citations · 2018
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