Papers
7
Total Citations
154
H-Index
5
About
Daniel Gordon is a leading researcher in the field of assistive robotics and human-robot interaction, with a primary focus on exoskeletons and rehabilitation technologies. His work centers on developing personalized, data-driven approaches to robotic assistance, particularly for human locomotion and balance recovery. Gordon’s most impactful contribution is his pioneering method for "Human-in-the-Loop Optimization of Exoskeleton Assistance," which uses online simulation of metabolic cost to tailor support to individual users—a paper that has garnered 96 citations. He has also made significant strides in quantifying exoskeleton performance across varied walking conditions (26 citations) and in establishing a framework for triadic human-robot collaboration (14 citations). Notably, Gordon’s research on unified push recovery fundamentals draws inspiration from human studies to improve humanoid robot balance, while his recent work on learning personalized sit-to-stand strategies via inverse musculoskeletal optimal control (5 citations) and designing personalized rehabilitation controllers (4 citations) underscores his commitment to individualized therapy. His model-based optimization methods for robot-assisted gait training (2 citations) further highlight his innovative approach to accelerating patient recovery. Gordon’s work is instrumental in bridging the gap between robotic systems and human variability, promising safer, more effective assistive technologies.
Research Focus
Key Achievements
Top Papers
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- 4Unified Push Recovery Fundamentals: Inspiration from Human Study7 citations · 2020
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