Jaein Kim

Papers

Total Citations

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About

Jaein Kim is a robotics researcher advancing human-robot interaction through language-guided manipulation and multi-agent systems. Their work centers on enabling robots to understand and act upon natural language instructions in real-world environments. Kim’s most cited paper, "GVCCI: Lifelong Learning of Visual Grounding for Language-Guided Robotic Manipulation" (2023, 6 citations), tackles the challenge of adapting pre-trained visual grounding models to manipulation settings, allowing robots to continuously learn and recognize objects without retraining from scratch. Building on this, "PROGrasp: Pragmatic Human-Robot Communication for Object Grasping" (2024, 4 citations) introduces a pragmatic approach to interactive grasping, where robots interpret human instructions beyond simple category labels, mirroring how people naturally communicate. Additionally, Kim’s work on "Robust Map Fusion with Visual Attention Utilizing Multi-agent Rendezvous" (2023, 2 citations) addresses multi-robot SLAM, enabling multiple agents to merge independently built maps during encounters. Together, these contributions demonstrate Kim’s impact in making robotic systems more adaptive, communicative, and collaborative—key steps toward robots that can work seamlessly alongside humans in dynamic settings.