Caroline Ponzoni Carvalho Chanel
Université Fédérale de Toulouse Midi-Pyrénées, Institut Superieur de l'Aeronautique et de l'Espace (ISAE-SUPAERO)
Papers
19
Total Citations
290
H-Index
9
About
Caroline Ponzoni Carvalho Chanel is a prominent researcher at the intersection of human-robot interaction, physiological computing, and autonomous decision-making under uncertainty. Her work addresses a critical gap in modern robotics: ensuring that increasingly automated systems remain genuinely responsive to the humans who work alongside them. Chanel has made seminal contributions to the development of mixed-initiative human-robot systems, in which task authority is dynamically redistributed between human operators and artificial agents based on real-time assessments of cognitive and physiological states. Her highly cited 2020 paper on physiological computing in HRI (52 citations) established a compelling framework for integrating mental state monitoring into robotic systems, while her work on mental workload estimation for pilot-UAV teaming (38 citations) demonstrates direct applicability to safety-critical aerospace environments. Earlier foundational work applying POMDP-based probabilistic planning to multi-target UAV missions (30 citations) highlights her long-standing expertise in autonomous decision-making under uncertainty. Across more than a decade of research, Chanel has consistently bridged cognitive science, robotics, and artificial intelligence, producing work that is both theoretically rigorous and practically grounded in real-world human-robot teaming scenarios.
Research Focus
Key Achievements
Top Papers
- 1How Can Physiological Computing Benefit Human-Robot Interaction?52 citations · 2020
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- 4Multi-Target Detection and Recognition by UAVs Using Online POMDPs30 citations · 2013
- 5A Robotic Execution Framework for Online Probabilistic (Re)Planning21 citations · 2014
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- 10Towards human-robot interaction: A framing effect experiment9 citations · 2016