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About

Gijo Sebastian is a robotics researcher whose work sits at the critical intersection of control theory, human-robot interaction, and rehabilitation engineering. His primary research areas include force estimation without physical sensors, iterative learning control (ILC), and constraint satisfaction for safe human-robot collaboration. Sebastian’s most impactful contribution is his 2019 paper on interaction force estimation using extended state observers, which has garnered 67 citations. This work demonstrated how to estimate external forces from joint position and actuation data alone, eliminating the need for expensive or fragile force/torque sensors—a breakthrough for assistive and rehabilitation robotics. His research on feedback-based iterative learning control with output constraints (26 and 11 citations respectively) directly addresses safety requirements for robotic systems working with vulnerable populations, such as stroke patients undergoing rehabilitation. Sebastian has developed and experimentally validated algorithms that ensure robotic manipulators respect hard position and safety constraints during repetitive therapeutic exercises. His work on the EMU upper-limb rehabilitation device showcases practical applications of his force observer and ILC techniques for detecting patient-applied forces without external sensors. Through his publications, Sebastian has established himself as a key contributor to making rehabilitation robotics safer, more accessible, and more effective for clinical use.