Gabin Paillet
Papers
3
Total Citations
10
H-Index
2
About
Gabin Paillet is an emerging researcher specializing in space robotics, reinforcement learning, and autonomous navigation systems, with a particular focus on the challenging domain of lunar exploration. His work centers on developing intelligent visuomotor and planning systems that enable robotic rovers to operate autonomously in complex, uncertain extraterrestrial environments. Paillet's most notable contribution is the SegVisRL framework, presented across two publications in 2021, which pioneered the application of reinforcement learning to visuomotor development for lunar rovers, integrating both proprioceptive and exteroceptive sensor data to achieve robust hazard avoidance using camera imagery. This biologically inspired approach draws parallels to natural visuomotor systems, demonstrating creative cross-disciplinary thinking. His earlier 2020 work introduced the RL STaR Platform, a simulation-based training environment designed to bridge the gap between reinforcement learning research and practical space robotics applications — addressing the stochasticity and uncertainty inherent in lunar cave exploration scenarios. With a cumulative citation count of 10 across his key publications, Paillet represents a promising voice in the growing field of private and autonomous space exploration robotics, contributing foundational tools at the intersection of deep learning, simulation, and planetary rover autonomy.
Research Focus
Key Achievements
Top Papers
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