Cesar Hernandez Reyes
Papers
5
Total Citations
46
H-Index
4
About
Cesar Hernandez Reyes is a leading researcher in bio-inspired robotics and olfactory search, specializing in chemical plume tracing (CPT) for autonomous systems. His work bridges neuroethology and robotics, using insects like silkworm moths as models to develop efficient odor-source localization algorithms. His most-cited paper (2019, 17 citations) analyzes the role of wind information in CPT, demonstrating that an optogenetic moth-inspired robot achieves higher search success and faster source location in low-frequency odorant environments compared to conventional algorithms. Reyes pioneered the "animal-in-the-loop" framework (2018, 15 citations), which models adaptive animal behavior while accounting for robot dynamics—a critical advance over static bio-inspired approaches. He further applied deep inverse reinforcement learning (2021, 7 citations) to extract generic olfactory search strategies from silk moths, enabling robots to detect drugs, gas leaks, and explosives. His work on sampling strategies (2020, 5 citations) introduced a flicking motion that improves plume tracing, and he investigated how agent size affects infotactic and hybrid searches (2021, 2 citations). By systematically decoding insect navigation and translating it into robust robotic algorithms, Reyes is advancing the practical deployment of autonomous chemical detectors in hazardous environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2Animal-in-the-loop system to investigate adaptive behavior15 citations · 2018
- 3
- 4
- 5