Thibault Rouillard
Papers
1
Total Citations
2
H-Index
1
About
Thibault Rouillard is a robotics researcher whose work focuses on integrating machine learning with autonomous systems, particularly in the domain of object retrieval in hazardous environments. His key research areas include supervised learning, reinforcement learning, and human-robot interaction, with a strong emphasis on developing cognitive capabilities for tele-operated robots. Rouillard’s major contribution lies in his pioneering approach to two-stage object retrieval, where he combines supervised and reinforcement learning to enable robots to autonomously navigate and manipulate objects in dangerous settings—reducing the need for direct human control. Although his most cited paper, "Autonomous Two-Stage Object Retrieval Using Supervised and Reinforcement Learning" (2019), has garnered 2 citations, its conceptual framework addresses a critical challenge in robotics: endowing machines with the cognitive ability to adapt to novel situations. This work highlights Rouillard’s commitment to advancing robot autonomy in real-world, high-stakes applications, such as disaster response and nuclear decommissioning. His research continues to influence the development of more intelligent, adaptable robotic systems that can operate independently in environments where human safety is at risk.
Research Focus
Key Achievements
Top Papers
- 1