Pangkit Fong

Papers

Total Citations

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About

Pangkit Fong is a researcher at the forefront of integrating Large Language Models (LLMs) with autonomous robotics, focusing on enhancing robotic adaptability and security in multi-agent systems. His key contributions center on developing self-correcting robotic planners and analyzing vulnerabilities in multi-robot coordination. In his most cited work, "HiCRISP: An LLM-Based Hierarchical Closed-Loop Robotic Intelligent Self-Correction Planner" (2024, 4 citations), Fong addresses a critical limitation in LLM-driven robotics: the inability to self-correct during task execution in dynamic environments. This work proposes a hierarchical framework that enables real-time error recovery, significantly advancing autonomous task planning and human-robot interaction. Additionally, his paper "Optimal Attack Against Coverage Path Planning in Multi-robot System" (2022, 3 citations) explores adversarial vulnerabilities in multi-robot systems, identifying optimal attack strategies to improve system resilience. Fong’s research bridges theoretical security analysis with practical robotic applications, offering insights that are vital for deploying robust, intelligent robots in real-world settings. His work is particularly notable for its timely response to the growing need for safe and adaptable autonomous systems, making him a promising voice in the field of robotic intelligence and cybersecurity.