Jilei Mao
Papers
2
Total Citations
16
H-Index
2
About
Jilei Mao is a leading researcher in robot manipulation and embodied intelligence, best known for pioneering the RoboMIND benchmark—a transformative resource in multi-embodiment robotics. Their flagship work, *RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation* (2025, 14 citations), introduces a massive dataset of 107,000 demonstration trajectories spanning 479 diverse tasks and 96 object classes, collected via human teleoperation. This foundational contribution addresses a critical gap in robotics by providing standardized, high-quality training data for generalist manipulation policies across different robot platforms. Mao’s research focuses on enabling robots to learn complex, real-world manipulation skills through scalable data collection and benchmarking, directly advancing the field toward more adaptable and intelligent robotic systems. Their work has already garnered significant attention for its potential to accelerate progress in imitation learning and reinforcement learning for robotics. By establishing normative data standards for multi-embodiment systems, Jilei Mao is shaping the future of how robots perceive, plan, and interact with their environments, making them a key figure to watch in embodied AI research.
Research Focus
Key Achievements
Top Papers
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