Hang Lai

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About

Hang Lai is a robotics researcher focused on advancing legged robot autonomy through innovative control and perception frameworks. His primary research areas include multi-embodiment robot control, adaptive locomotion under hardware faults, and world model-based visual perception. Lai’s major contributions include pioneering the use of sequence modeling for multi-embodiment legged robot control, enabling robots to adapt their behavior across different physical forms without retraining—a breakthrough published in 2023 with 11 citations. He also developed an adaptive control strategy for quadruped robots experiencing actuator degradation (7 citations), addressing a critical gap in robot resilience for real-world deployment. His most recent work (2025, 5 citations) introduces a world model-based perception system that integrates proprioception and vision for more data-efficient visual legged locomotion. Lai’s research directly tackles the practical challenges of deploying legged robots in unstructured environments, from hardware faults to complex terrain navigation. His work has been recognized for its potential to make robots more robust, adaptable, and practical for field applications, positioning him as an emerging leader in legged robotics and embodied AI.