Max Robotics — Global Service Robot Directory + US Certification

About

Yunxin Mao is an emerging robotics researcher whose work sits at the intersection of robot planning, control architectures, and embodied intelligence. Mao's primary contributions center on Behavior Tree (BT) planning — a sophisticated approach to encoding robot decision-making that prioritizes modularity, robustness, and safety. Their landmark paper, "MRBTP: Efficient Multi-Robot Behavior Tree Planning and Collaboration" (2025), tackles the formidable challenge of extending BT planning from single-robot to multi-robot systems, earning 3 citations in its debut year and signaling strong early interest from the community. Complementing this, Mao has advanced the field of natural language-guided robot control through work on intent understanding and optimal BT generation from human instructions, bridging the gap between everyday communication and reliable robot behavior. Their research also spans robot morphology optimization, demonstrated in "Task2Morph" (2023), a differentiable framework for contact-aware robot design that addresses embodied intelligence through task-inspired co-optimization of shape and control. The release of BTPG, a dedicated benchmark platform for BT planning in service robots, further reflects Mao's commitment to reproducible, community-driven robotics research. Though early in their career, Mao's focused and multifaceted contributions position them as a promising voice in next-generation autonomous systems.