Papers
40
Total Citations
589
H-Index
15
About
Luis Figueredo is a robotics researcher whose work spans robust control theory, human-robot collaboration, natural language interfaces for robotics, and telemedicine systems. His early foundational contribution — a 2013 paper on H∞ robust kinematic control using dual quaternion representation (54 citations) — established him as an authority in mathematically elegant approaches to manipulator control, work he later extended in a 2021 journal publication. Figueredo has since pioneered the integration of natural language processing with robot motion generation, developing influential frameworks such as the trajectory reshaping study (43 citations) and LATTE, the LAnguage Trajectory TransformEr (42 citations), which bridge the gap between human intent and low-level robotic motion. His 2025 comprehensive review of sampling-based motion planning (41 citations) reflects his breadth across the field. Notably, Figueredo has made meaningful contributions to healthcare robotics, including tactile telemedicine systems developed during the COVID-19 pandemic and a dual doctor-patient digital twin paradigm for remote diagnosis and rehabilitation. His work on ergonomics-informed metrics for physical human-robot collaboration further demonstrates his commitment to human-centred robotics. Across more than a decade of research, Figueredo has consistently bridged theoretical rigour with real-world applicability.
Research Focus
Key Achievements
Top Papers
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- 3LATTE: LAnguage Trajectory TransformEr42 citations · 2023
- 4Motion planning for robotics: A review for sampling-based planners41 citations · 2025
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- 10A Solution to Slosh-free Robot Trajectory Optimization22 citations · 2022