Papers
20
Total Citations
282
H-Index
7
About
Ali Shafti is a robotics researcher whose work sits at the intersection of assistive technology, human augmentation, and human-robot interaction. His research addresses some of the most pressing challenges in modern robotics: how machines can seamlessly extend, restore, or collaborate with human capabilities. Shafti's most influential contribution — his gaze-based assistive robotic system (2019, 68 citations) — demonstrated how eye-tracking could translate high-level human intent into intuitive robotic reaching and grasping, offering new independence to people with movement disabilities. Complementing this, his work on embroidered EMG electrodes (2016, 29 citations) and conductive yarn-based shape sensing (2015, 52 citations) reflects a sophisticated understanding of wearable and soft robotics hardware, pushing sensing technologies toward practical, unobtrusive solutions. Perhaps most imaginatively, Shafti has explored the frontier of human augmentation through his Supernumerary Robotic 3rd Thumb projects (2018, 2021; 55 combined citations), investigating whether the human motor system can genuinely incorporate an extra robotic digit — tested through demanding tasks like piano performance. His work on deep reinforcement learning for human-robot collaboration further underscores his broad technical range. Across his portfolio, Shafti consistently bridges rigorous engineering with deeply human-centred applications.
Research Focus
Key Achievements
Top Papers
- 1Gaze-based, context-aware robotic system for assisted reaching and grasping68 citations · 2019
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- 4Designing embroidered electrodes for wearable surface electromyography29 citations · 2016
- 5The Supernumerary Robotic 3<sup>rd</sup> Thumb for Skilled Music Tasks24 citations · 2018
- 6FourByThree: Imagine humans and robots working hand in hand21 citations · 2016
- 7A gaze-contingent intention decoding engine for human augmentation8 citations · 2018
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