Papers
8
Total Citations
70
H-Index
6
About
Yunho Choi is a robotics researcher whose work spans the intersection of machine learning, control, and autonomous navigation. His key research areas include robot path planning under uncertainty, visual navigation, and human-robot interaction through language and action synthesis. Choi has made significant contributions to safe and robust path planning, introducing chance-constrained multilayered sampling-based methods that ensure mission completion even under noise and disturbances—a framework that has garnered over 20 cumulative citations. He has also advanced image-goal navigation by developing a keypoint-based reinforcement learning approach that overcomes field-of-view limitations and obstacle avoidance, cited 11 times. Notably, his work on Text2Action pioneered the use of generative adversarial networks to translate natural language descriptions into human action sequences, bridging the gap between linguistic commands and robotic behavior. More recently, Choi has explored commonsense reasoning for object goal navigation, creating an object value graph that enhances a robot’s ability to find targets in unseen environments. His research, with over 70 total citations, demonstrates a consistent focus on enabling robots to operate intelligently and safely in complex, real-world settings.
Research Focus
Key Achievements
Top Papers
- 1
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- 3Image-Goal Navigation via Keypoint-Based Reinforcement Learning11 citations · 2021
- 4Text2Action: Generative Adversarial Synthesis from Language to Action8 citations · 2018
- 5Hierarchical 6-DoF Grasping with Approaching Direction Selection7 citations · 2020
- 6Commonsense-Aware Object Value Graph for Object Goal Navigation6 citations · 2024
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