Yifang Liu

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About

Yifang Liu is a robotics researcher whose work centers on autonomous construction, multi-modal perception, and error handling in collective robotic systems. Their most impactful contribution, "Planning for Robotic Dry Stacking with Irregular Stones" (2021, 14 citations), addresses the challenge of enabling robots to autonomously assemble structures from unprocessed, irregular materials—a critical step toward practical, on-site construction automation. Liu further advanced robotic manipulation through their study on peg-in-a-hole insertion tasks (2020, 9 citations), where they demonstrated how behavioral cloning can integrate vision, force/torque, and proprioceptive data to overcome spatial uncertainty, a fundamental problem in precision assembly. Their ongoing exploration of errors in collective robotic construction (2022) highlights a commitment to understanding failure modes in multi-robot systems, essential for scaling up autonomous building. With a research portfolio that bridges perception, planning, and real-world deployment, Liu is shaping the future of construction robotics, where machines must adapt to unstructured environments and imperfect materials. Their work offers valuable insights for students and researchers interested in the intersection of machine learning, sensor fusion, and field robotics.