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About

Lois Liow is a rising star in soft robotics, pushing the boundaries of how we design, model, and control compliant machines for real-world manipulation. Her research centers on three key areas: topology optimization for soft grippers, physics-informed neural networks (PINNs) for modeling complex deformations, and novel actuation mechanisms like fiber jamming. Her most cited work, "Diversity‐Based Topology Optimization of Soft Robotic Grippers" (26 citations), introduces a computational framework to navigate the vast design space of multi-material printing, enabling bespoke grippers for complex objects. She further advanced modeling with "PINN-Ray" (11 citations), which uses neural networks to achieve high-accuracy, fast-inference surrogate models for soft Fin Ray fingers—a critical step toward safe, real-time control. Her contributions extend to underactuated grasping, where she analyzed tendon routing's impact on joint torque (11 citations), and to bio-inspired locomotion with a compliant robotic leg using fiber jamming (10 citations). Most recently, her work "DexGrip" showcases a multi-modal soft gripper capable of both dexterous grasping and in-hand manipulation, signaling a shift toward versatile, general-purpose soft hands. With over 60 total citations in just a few years, Liow is rapidly establishing herself as a leader in computational design and modeling for soft robotics.