Haeone Lee
Haeone Lee is an MS student at the Korea Advanced Institute of Science and Technology (KAIST), where he conducts research in robot learning under advisor Kimin Lee. He is the lead author and corresponding contact for the SPACE framework, which enables cross-robot policy learning by using Cartesian state deltas as a universal action representation. He has also published work on influence functions for data-centric robot learning and for understanding the impact of human feedback in RLHF.
“Haeone Lee - Lab/Institution: KAIST. Why notable: Lead author and corresponding contact. Driving the research on cross-robot generalization and the core architecture of the SPACE framework.”
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