Papers
15
Total Citations
194
H-Index
7
About
Meibao Yao is a leading researcher in modular robotics, reconfiguration planning, and autonomous systems for unstructured environments. Their work centers on enabling robots to adapt their shape, behavior, and control strategies in real time—critical for applications ranging from planetary exploration to on-orbit manipulation. Yao’s most cited paper, “A reconfiguration strategy for modular robots using origami folding” (2018, 50 citations), introduced a novel approach to overcoming mechanical failure from recurrent connectivity changes, advancing the reliability of self-reconfiguring systems. Another highly influential work, “Light4Mars” (2024, 43 citations), presents a lightweight transformer model for semantic segmentation in challenging terrains like Mars, demonstrating Yao’s commitment to AI-driven robotics for space exploration. Their research also includes co-optimization of morphology and behavior via hierarchical deep reinforcement learning (2023), and the creation of the M2CS multimodal dataset (2024, 16 citations) for dynamic SLAM and moving object perception. With contributions spanning fault tolerance, torque minimization, and terrain-guided optimization, Yao’s work has garnered over 180 citations, establishing them as a key innovator in adaptive, resilient robotic systems for extreme environments.
Research Focus
Key Achievements
Top Papers
- 1A reconfiguration strategy for modular robots using origami folding50 citations · 2018
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10Towards Peak Torque Minimization for Modular Self-Folding Robots5 citations · 2018