Papers
8
Total Citations
53
H-Index
4
About
Xifeng Gao is a leading researcher in robotics and computational design, whose work bridges multi-agent systems, trajectory optimization, and mechanical linkage synthesis. His most impactful contributions address the fundamental challenge of coordinating multiple robots in complex environments. In his highly cited 2021 work (16 citations), Gao pioneered a decentralized, learning-based approach using graph neural networks to solve the unlabeled multi-agent navigation problem, enabling robots to simultaneously handle goal assignment, collision avoidance, and navigation in obstacle-rich settings. Building on this, his 2022 paper (15 citations) introduced a robust ADMM-based framework for multi-robot trajectory optimization that breaks joint problems into efficient, decentralized sub-problems. Gao has also made notable advances in mechanical design, developing algorithms for the joint optimization of planar linkage topology and trajectory (9 citations), allowing users to specify end-effector paths and automatically generate optimal linkage structures. His work on soft robot control using reduced-order models and stress-minimizing grasp planning further demonstrates his versatility. With a growing citation record and publications spanning from 2019 to 2024, Gao continues to push boundaries in autonomous systems, optimization, and robotic manipulation.
Research Focus
Key Achievements
Top Papers
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- 3Joint search of optimal topology and trajectory for planar linkages9 citations · 2022
- 4Learning Reduced-Order Soft Robot Controller4 citations · 2023
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- 7Generating Optimal Grasps Under A Stress-Minimizing Metric2 citations · 2019
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