Francesco Braghin
Papers
57
Total Citations
1,423
H-Index
18
About
Francesco Braghin is a prominent robotics and control systems researcher whose work sits at the intersection of human-robot collaboration, rehabilitation engineering, and advanced control theory. His research has made substantial contributions to impedance control, reinforcement learning-based robotics, and wearable assistive devices, collectively garnering over 800 citations across his most influential publications. Braghin's early work established rigorous foundations in force-tracking impedance control, with his 2015 paper on optimal impedance design (123 citations) addressing critical challenges in robot-environment interaction for delicate industrial tasks. He subsequently pioneered iterative and reinforcement learning approaches to robotic force control, enabling manipulators to adapt to complex, partially unknown tasks with high precision. His 2020 model-based reinforcement learning framework for variable impedance control (192 citations) stands as his most celebrated contribution, reshaping how robots dynamically respond to human partners. Equally significant is Braghin's commitment to rehabilitation robotics. His systematic reviews on upper-limb exosuits and patient-cooperative control strategies have become essential references for clinicians and engineers alike, while the AGREE exoskeleton exemplifies his translational vision—bringing compliant, adaptive robotic assistance directly to post-stroke patients. Across industrial automation and medical robotics, Braghin's work consistently bridges theoretical rigor with meaningful real-world impact.
Research Focus
Key Achievements
Top Papers
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- 2Upper limb soft robotic wearable devices: a systematic review131 citations · 2022
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- 9Dynamic analysis of high precision construction cable-driven parallel robots45 citations · 2019
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