Mengqi He

Papers

Total Citations

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About

Mengqi He is a researcher focused on the intersection of soft robotics and autonomous navigation, with key contributions in pneumatic actuation and deep reinforcement learning (DRL). He developed a novel soft pneumatic crawling robot that leverages unbalanced inflation to achieve locomotion, demonstrating the potential of compliant, safe robots for human-robotic interaction and unstructured environments. This work, published in 2020, has garnered 3 citations and highlights his ability to innovate in soft robotic design. More recently, He has advanced the field of active SLAM by integrating DRL with intrinsic rewards to optimize goal decision-making using 2D LiDAR data. His 2024 paper, with 2 citations, addresses critical challenges in laser-based simultaneous localization and mapping, improving odometry estimation and mapping efficiency for industrial and service applications. By combining soft robotics with intelligent navigation, He bridges two dynamic research areas, offering solutions that enhance both robot safety and autonomy. His work is particularly notable for its practical implications in real-world environments, making him a promising early-career researcher with growing impact.