Francisco Munguia‐Galeano

Cardiff University, University of Liverpool

Papers

Total Citations

H-Index

About

Francisco Munguia-Galeano is a robotics researcher whose work sits at the intersection of human-robot interaction, autonomous manipulation, and self-driving laboratories. His primary research focuses on enabling robots to perform precise, adaptive tasks in dynamic environments—particularly through affordance-based reasoning and reinforcement learning. In his most cited work, "Affordance-Based Human–Robot Interaction With Reinforcement Learning" (2023, 14 citations), he addresses the challenge of grasp and release operations during human collaboration, proposing a framework that allows robots to learn manipulation policies from environmental cues. This contribution is foundational for smoother, safer physical human-robot collaboration. Munguia-Galeano also advances tactile sensing for handover tasks and, more recently, co-developed the LIRA module (Localization, Inspection, and Reasoning) for self-driving laboratories (2025, 2 citations). This work introduces closed-loop error detection and correction into autonomous experimentation workflows—a critical step toward reliable, high-throughput scientific discovery. His research bridges machine learning, sensor integration, and robotic control, with clear applications in manufacturing, healthcare, and automated science. Munguia-Galeano’s growing citation record reflects the timeliness and practical relevance of his contributions to next-generation robotic systems.