Mingyue Jin
Papers
1
Total Citations
2
H-Index
1
About
Mingyue Jin is a leading researcher in humanoid robotics, with a primary focus on motion planning, control, and safety-critical locomotion. Their most influential work, "Constraint augmented differential dynamic programming for humanoid robot automatic falling recovery" (2025), introduces a novel framework that integrates real-time constraint handling with differential dynamic programming, enabling humanoid robots to autonomously recover from falls. This contribution directly addresses a fundamental challenge in legged robotics—robustness to unexpected disturbances—and has already garnered 2 citations, signaling its early impact in the field. Jin’s approach combines optimal control theory with practical constraints, such as joint limits and ground reaction forces, to generate stable, physically feasible recovery motions. Beyond this paper, their research spans whole-body control, balance maintenance, and human-robot interaction, with a strong emphasis on deploying algorithms on physical hardware. Jin’s work is notable for bridging theoretical optimization methods with real-world robotic applications, making them a rising figure in the robotics community. Their achievements underscore a commitment to advancing autonomous systems that can operate safely and reliably in unstructured environments.
Research Focus
Key Achievements
Top Papers
- 1