Max Robotics — Global Service Robot Directory + US Certification

About

Yasaman Vaghei’s research lies at the intersection of reinforcement learning, neural networks, and bio-inspired control, with a strong focus on advancing robotic locomotion and brain-computer interfaces. Her early work established foundational methods for applying neural network reinforcement learning to bipedal robot walking control, including both 3-link and 5-link bipedal robots, demonstrating how actor-critic architectures can enable adaptive, stable gait patterns. These contributions, which have garnered over 20 combined citations, helped bridge the gap between adaptive optimal control and biologically plausible learning techniques. Vaghei also explored adaptive fuzzy hierarchical terminal sliding-mode control for under-actuated nonlinear robots, addressing complex trajectory tracking challenges in systems like flexible manipulators. More recently, her research has expanded into brain-computer interfaces, where she investigates decoding brain signals to anticipate gait direction—a promising step toward lower-limb exoskeleton control for rehabilitation. This work highlights her commitment to translating reinforcement learning and neural control principles into real-world assistive technologies. With a career spanning foundational robotics control to cutting-edge neural decoding, Vaghei continues to make impactful contributions that connect machine learning, robotics, and neurorehabilitation.