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About

Tanishka Goyal is a leading researcher at the intersection of robotics, control systems, and neurorehabilitation, with a focus on developing intelligent, compliant robotic systems for physical therapy. Her primary research areas include impedance control, system identification, and adaptive control for rehabilitation robots, particularly for wrist and gait rehabilitation. Goyal’s major contributions involve pioneering the use of Koopman operator theory to linearize nonlinear human-robot interaction dynamics, enabling more precise and safe control of intrinsically compliant robots. Her work on autodidact stiffness learning and stiffness-observer-based adaptive control has advanced the field by allowing robots to autonomously adapt to patient-specific needs, enhancing both safety and therapeutic efficacy. With over 70 citations across her most-cited papers, her 2022 studies on wrist rehabilitation robots have garnered significant attention, each accumulating 19–21 citations. Notably, her 2024 paper on transformer-based transactive energy prediction in neurorehabilitation represents a cutting-edge integration of AI for personalized therapy. Goyal’s innovative approaches, including quantum-driven neuromechanical control and nonlinear model predictive control for gait rehabilitation, position her as a transformative figure in robotic-assisted therapy, pushing boundaries toward safer, more adaptive, and patient-centered rehabilitation technologies.