Papers
15
Total Citations
298
H-Index
8
About
John Nassour’s research lies at the intersection of bio-inspired robotics, soft wearable technologies, and human-robot interaction, with a focus on enabling adaptive, resilient, and human-like movement in machines. His most influential work introduces a multi-layered, multi-pattern central pattern generator (CPG) that allows humanoid robots to autonomously generate and switch between diverse locomotion and upper-body motor patterns—a foundational contribution cited over 88 times. Nassour has also pioneered soft wearable robotics, developing a robust data-driven sensory glove for identifying and replicating human hand motions (42 citations) and designing high-performance, enfolded-textile pneumatic actuators that deliver controllable forces for lightweight, comfortable wearable robots (35 citations). His work extends to reinforcement learning frameworks for fall recovery and perturbation resistance in bipedal robots, as well as neuroscience-inspired models that unify action representation for both locomotion and manipulation. With more than 270 cumulative citations across his top papers, Nassour’s contributions are shaping the next generation of adaptive humanoid robots and soft, textile-based assistive devices—bridging fundamental neuroscience, machine learning, and practical wearable engineering.
Research Focus
Key Achievements
Top Papers
- 1Multi-layered multi-pattern CPG for adaptive locomotion of humanoid robots88 citations · 2014
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- 5A Humanoid Robot Learns to Recover Perturbation During Swinging Motion22 citations · 2018
- 6Enfolded Textile Actuator for Soft Wearable Robots21 citations · 2019
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- 8Concrete Action Representation Model: From Neuroscience to Robotics11 citations · 2019
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- 10Learning of Central Pattern Generator Coordination in Robot Drawing8 citations · 2018