Papers
16
Total Citations
221
H-Index
5
About
Laurent Jeanpierre’s research lies at the intersection of multi-robot coordination, human-robot collaboration, and decision-theoretic planning under uncertainty. His most cited work (100 citations) introduces a decentralized Markov decision process framework for multi-robot exploration under communication constraints, enabling teams to coordinate effectively even with limited bandwidth. He has made foundational contributions to adjustable autonomy, developing Mixed Markov Decision Processes that allow robots to dynamically shift control between autonomous operation and human supervision—a critical capability for service robots in unpredictable environments. His 2012 paper on distributed value functions for multi-robot exploration (29 citations) remains influential in the field of decentralized exploration. Jeanpierre also pioneered frameworks for human-robot collaboration without shared plans, addressing the challenging scenario where robots must infer and adapt to human intentions in real time. His work on robust decision-theoretic planning for service robots (2016) provides practical tools for deploying robots in populated spaces like shopping malls. With over 200 total citations, Jeanpierre’s research has shaped how autonomous systems manage uncertainty, coordinate with humans, and operate in complex, real-world environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2Distributed value functions for multi-robot exploration29 citations · 2012
- 3
- 4A Decision-Theoretic Approach to Cooperative Control and Adjustable Autonomy15 citations · 2010
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- 7Human-robot collaboration for a shared mission5 citations · 2010
- 8
- 9Optimizing Requests for Support in Context-Restricted Autonomy4 citations · 2021
- 10