Thibaut Deleruyelle
Papers
1
Total Citations
5
H-Index
1
About
Thibaut Deleruyelle is a researcher at the forefront of embedded robotics and adaptive control systems, with a focus on integrating large language models (LLMs) with reinforcement learning for real-time autonomous navigation. His most notable contribution is the development of a hybrid LLM Q-learning/deep Q-network (DQN) framework, first introduced in his 2025 paper, which has already garnered 5 citations. This pioneering work demonstrates how a locally deployed LLM can enhance obstacle avoidance by enabling an STM32WB55RG microcontroller to make context-aware, real-time decisions using sensor data—a significant leap in bridging natural language processing with resource-constrained embedded systems. Deleruyelle’s research addresses critical challenges in adaptive robotics, offering a scalable solution for safer, more intelligent autonomous agents. His achievements highlight a unique interdisciplinary approach, combining reinforcement learning, edge computing, and AI, positioning him as an emerging leader in the field. For students and researchers, his work exemplifies how cutting-edge AI can be practically deployed in low-power environments, opening new avenues for next-generation robotic systems.
Research Focus
Key Achievements
Top Papers
- 1