Papers
6
Total Citations
29
H-Index
4
About
Adna Bliek’s research sits at the intersection of human-robot interaction, assistive robotics, and skill transfer, with a focus on making robots more intuitive, adaptive, and trustworthy for real-world collaboration. Her most cited work, “Interactive Human–Robot Skill Transfer: A Review of Learning Methods and User Experience” (2021, 9 citations), provides a comprehensive framework for generalizing robot behavior across dynamic tasks by integrating learning from demonstration with transfer learning and user feedback—a foundational contribution to the field. Bliek also advances wearable robotics, notably in “Compensating elastic faults in a torque-assisted knee exoskeleton” (2024, 5 citations), where she addresses the critical challenge of fault tolerance in elastic actuators, enhancing safety and user perception. Her innovative “high-accuracy, low-budget Sensor Glove for Trajectory Model Learning” (2021, 5 citations) democratizes motion capture for VR and research, while her work on robot-triggered backchanneling (2020, 5 citations) explores how machines can naturally cue human responses. With over 30 citations across her portfolio, Bliek’s contributions are shaping next-generation human-robot systems that are both capable and socially aware.
Research Focus
Key Achievements
Top Papers
- 1
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- 3A high-accuracy, low-budget Sensor Glove for Trajectory Model Learning5 citations · 2021
- 4How Can a Robot Trigger Human Backchanneling?5 citations · 2020
- 5
- 6Measuring, modeling and fostering embodiment of robotic prosthesis1 citations · 2024