48 citations · 70 across the 6 of their papers we have counts for
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cs.RO2020★ 3 cited
PBCS : Efficient Exploration and Exploitation Using a Synergy between Reinforcement Learning and Motion Planning
Guillaume Matheron, Nicolas Perrin, Olivier Sigaud
The exploration-exploitation trade-off is at the heart of reinforcement learning (RL). However, most continuous control benchmarks used in recent RL research only require local exp…
cs.RO2018★ 48 cited
Fast Approximate Clearance Evaluation for Rovers with Articulated Suspension Systems
Kyohei Otsu, Guillaume Matheron, Sourish Ghosh +2
We present a light-weight body-terrain clearance evaluation algorithm for the automated path planning of NASA's Mars 2020 rover. Extraterrestrial path planning is challenging due t…