Dennis Majoe
Papers
6
Total Citations
36
H-Index
3
About
Dennis Majoe is a robotics and embedded systems researcher whose work spans bipedal locomotion, wearable robotics, and reconfigurable computing platforms. His research is particularly distinguished by its focus on making autonomous robots lighter, more energy-efficient, and capable of operating safely alongside humans. Majoe's most impactful contribution lies in bipedal robot balance control, where his 2021 paper on hybrid autonomous controllers combining deep reinforcement learning with pattern generators has garnered 18 citations — a testament to the growing relevance of intelligent, adaptive locomotion in human-robot collaborative environments. This work builds on earlier foundations, including his 2020 research extending Normalized Advantage Functions with recurrent neural networks for minimal-power balancing strategies. Beyond locomotion intelligence, Majoe has pioneered novel actuation and design approaches, including advancements in Pneumatic Air Muscles for lightweight bipedal robots and the development of S.A.R.A.H., an electromagnet-driven bipedal platform supported by Innovate UK funding. His work on wearable exoskeletons for rehabilitation and industry further demonstrates his commitment to translational robotics. Complementing these efforts, his contributions to multi-core FPGA computing highlight a strong systems-level perspective that underpins his broader research vision.
Research Focus
Key Achievements
Top Papers
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- 3Power and endurance for comfortable wearable robotics3 citations · 2014
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