Papers
3
Total Citations
15
H-Index
2
About
Ike Obi is a researcher at the forefront of human-robot collaboration, with key contributions in preference-based reinforcement learning and adaptive task allocation for multi-agent systems. His work addresses two critical challenges in robotics: reducing the human feedback burden in robot training and optimizing coordination in heterogeneous human-robot teams. In his highly cited 2025 paper "PrefCLM: Enhancing Preference-Based Reinforcement Learning With Crowdsourced Large Language Models" (9 citations), Obi pioneered a method that leverages crowdsourced large language models to dramatically reduce the volume of human feedback needed for teaching robots through comparative preferences, bypassing complex reward engineering. His parallel work on "Adaptive Task Allocation in Multi-Human Multi-Robot Teams Under Team Heterogeneity and Dynamic Information Uncertainty" (4 citations in 2025, 2 in 2024) introduces novel frameworks that simultaneously address team member diversity, dynamic task execution, and information uncertainty—challenges that existing approaches typically handle in isolation. With a growing citation impact, Obi's research is shaping the future of scalable, human-aware robotic systems, making him a notable emerging voice in the field of collaborative robotics and multi-agent systems.
Research Focus
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Top Papers
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