Kazuki Hori
Papers
2
Total Citations
14
H-Index
2
About
Kazuki Hori is at the forefront of integrating large language models (LLMs) with robotic task planning, a field where he has rapidly established himself as a key innovator. His research focuses on bridging the gap between natural language instructions and actionable robotic commands, addressing critical challenges in ambiguity, uncertainty, and long-horizon planning. Hori’s most cited work, "Interactively Robot Action Planning with Uncertainty Analysis and Active Questioning by Large Language Model" (2024, 11 citations), pioneers a method where robots actively query for clarification, enabling more robust and context-aware decision-making. Building on this, his 2025 paper "Enhancement of long-horizon task planning via active and passive modification in large language models" (3 citations) tackles the limitation of overly simplistic plans, proposing techniques to generate complex, multi-step action sequences. Though early in his career, Hori’s contributions are already shaping how robots interpret human intent, moving beyond static commands to dynamic, interactive planning. His work is essential reading for anyone exploring the intersection of LLMs and robotics, offering practical frameworks for making autonomous systems more adaptive and intelligent in real-world environments.
Research Focus
Key Achievements
Top Papers
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- 2