Suyeon Shin
Papers
2
Total Citations
9
H-Index
2
About
Suyeon Shin is a rising researcher at the forefront of embodied AI and language-guided robotics, dedicated to bridging the gap between human instructions and robotic action. Her work centers on two critical challenges: enabling robots to understand and manipulate objects in real-world environments through visual grounding, and empowering them to plan complex tasks without extensive supervision. In her highly cited 2023 paper, "GVCCI: Lifelong Learning of Visual Grounding for Language-Guided Robotic Manipulation," Shin tackles the problem of domain adaptation, showing how robots can continuously learn to recognize objects in new settings—a vital step toward practical, deployable robotics. Building on this, her 2025 work, "Socratic Planner: Self-QA-Based Zero-Shot Planning for Embodied Instruction Following," introduces a novel, self-questioning approach that allows robots to plan compositional tasks from scratch, sidestepping the need for costly annotated data. With over 9 citations already, Shin’s contributions are shaping a future where robots can learn and adapt on the fly, making her a compelling voice for students interested in the intersection of computer vision, natural language processing, and autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2