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About

Lasitha Weerakoon is a rising roboticist whose work sits at the intersection of soft robotics, control theory, and human-robot interaction. Their research focuses on enabling precise, reliable control of soft and hybrid rigid-soft (HyRiSo) robotic systems—a challenging domain due to the inherent flexibility and underactuation of these platforms. Weerakoon’s major contributions include pioneering bilateral teleoperation of soft manipulators under the piecewise constant curvature hypothesis (11 citations), developing adaptive tracking control using integrated sensing skins and recurrent neural networks (7 citations), and introducing novel systems like the soft inverted pendulum with a revolute base (5 citations) and HyRiSo robots that combine the dexterity of soft links with the support of rigid ones (3 citations). Their work addresses critical challenges in proprioception, model-based control, and passivity under parametric uncertainty. By advancing closed-loop control for soft robots, Weerakoon is helping bridge the gap between theoretical modeling and practical deployment in remote manipulation and assistive applications. Their research is particularly notable for integrating sensing and learning-based methods to overcome the limitations of traditional control in soft systems.