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About

Philippe Morere is a robotics researcher whose work sits at the intersection of probabilistic planning, Bayesian optimization, and autonomous systems. His research focuses on enabling robots to make intelligent decisions under uncertainty, with particular emphasis on motion planning, environment monitoring, and partially observable systems. Morere's most influential contribution, "Sequential Bayesian Optimization as a POMDP for Environment Monitoring with UAVs" (2017, 54 citations), elegantly bridges Bayesian optimization with the POMDP framework, allowing unmanned aerial vehicles to plan observations more effectively by accounting for real-world robotic constraints. This work established him as a key voice in applying probabilistic decision-making to autonomous platforms. His follow-up work on continuous state-action-observation POMDPs (2018, 17 citations) extended these ideas to richer, more realistic planning scenarios. His 2020 paper on Bayesian Local Sampling-Based Planning (43 citations) represents another significant contribution, addressing fundamental inefficiencies in motion planning by replacing global random sampling with principled Bayesian local strategies — improving both sample efficiency and practical performance. Complementing this, his curriculum-based hierarchical planning work (2019) demonstrates a broader interest in structured, learnable planning frameworks. Across his publications, Morere has accumulated over 125 citations, reflecting genuine and growing impact in the robotics planning community.