Joonsig Gong
Papers
2
Total Citations
58
H-Index
2
About
Joonsig Gong is a robotics researcher whose work focuses on advancing motion planning for complex, high-dimensional systems, with a particular emphasis on humanoid robots. His key research areas include sampling-based path planning, cost-aware navigation, and energy-efficient motion optimization. Gong’s most notable contribution is his 2017 paper, "Fast Sampling-Based Cost-Aware Path Planning With Nonmyopic Extensions Using Cross Entropy," which has garnered 53 citations. In this work, he introduced a novel method that integrates cross-entropy optimization with nonmyopic planning strategies, enabling robots to navigate complex, realistic environments more efficiently by considering long-term costs rather than just immediate gains. This approach significantly improves the practicality of sampling-based algorithms like RRT for real-world applications. Additionally, his 2015 paper on "Energy-efficient high-dimensional motion planning for humanoids" tackles the critical challenge of minimizing energy consumption during manipulation tasks, a key step toward sustainable and autonomous humanoid operation. Through these contributions, Gong has helped bridge the gap between theoretical planning algorithms and their deployment in energy-constrained, dynamic settings.
Research Focus
Key Achievements
Top Papers
- 1
- 2