Camille Phiquepal
Papers
1
Total Citations
5
H-Index
1
About
Camille Phiquepal is a robotics researcher whose work lies at the intersection of motion planning, perception, and decision-making under uncertainty. Her primary research focuses on developing algorithms that enable robots to navigate and act in partially observable environments—spaces where critical information, such as the state of doors or obstacles, is hidden and must be inferred. Her most cited work, "Path-Tree Optimization in Discrete Partially Observable Environments Using Rapidly-Exploring Belief-Space Graphs" (2022), introduces a novel framework that combines sampling-based planning with belief-space reasoning to handle multi-modal problems. This approach allows robots to efficiently explore and exploit discrete environmental features, such as determining whether a door is open or closed, while optimizing long-horizon paths. Though early in her career, her contributions are already shaping how robots tackle real-world challenges in logistics, search-and-rescue, and autonomous navigation. With 5 citations on this foundational paper, Phiquepal’s work is gaining traction for its practical elegance and theoretical depth, marking her as a promising voice in the field of robotic autonomy.
Research Focus
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Top Papers
- 1