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About

Tianqi Wei is a pioneering researcher at the intersection of soft robotics and bio-inspired control systems. Their work is defined by two core thrusts: developing compliant, safe-to-interact soft machines, and creating neural network architectures that mirror biological locomotion. Wei’s most cited work, “A Soft Pneumatic Maggot Robot” (2016, 12 citations), introduced a novel approach to soft actuation, demonstrating how low-modulus materials can replicate the undulating motion of insect larvae for exploration in confined spaces. This was complemented by “Integrating soft sensor systems using conductive thread” (2018, 11 citations), which advanced the field by embedding sensing directly into soft structures, enabling proprioception without rigid components. On the computational side, Wei’s “A Bio-inspired Reinforcement Learning Rule to Optimise Dynamical Neural Networks for Robot Control” (2018, 7 citations) broke from backpropagation by proposing a learning rule suited for time-varying neural circuits, a key step toward truly autonomous soft robots. Their most recent work, “Bio-inspired neural networks with central pattern generators for learning multi-skill locomotion” (2025, 6 citations), synthesizes these threads, using spinal-cord-inspired CPGs to enable multi-gait learning in high-DOF robots like centipedes. With a growing citation footprint and a clear trajectory from component design to system-level intelligence, Wei is shaping a future where robots are both soft-bodied and neurally agile.