Alisha Fong

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About

Alisha Fong is an emerging researcher at the intersection of robotics, artificial intelligence, and natural language processing, with a focused expertise in lifelong robot learning and large language model (LLM)-based planning systems. Her most notable contribution centers on addressing a critical limitation in current LLM-driven robotic planners — their inability to adapt beyond a fixed set of pre-defined skills. Through her work on human-assisted language planners, Fong has pioneered a methodology that enables robots to continuously expand their skill repertoire over time, bridging the gap between static AI systems and truly adaptive robotic agents. This research, which has accumulated 14 citations across its 2023 and 2024 iterations, demonstrates both the timeliness and growing recognition of her contributions within the robotics and AI communities. By integrating human assistance into the learning loop, Fong's approach makes robot learning more scalable and practical for real-world deployment. Her work speaks directly to one of the most pressing challenges in modern robotics: creating systems that evolve intelligently alongside human needs, marking her as a promising voice in the future of human-robot collaboration.