Papers
3
Total Citations
17
H-Index
2
About
Baptiste Pelletier is a researcher at the forefront of formal methods for autonomous robotics, specializing in the verification and validation of skill-based robotic systems. His work centers on ensuring the reliability and safety of autonomous agents operating in dynamic, remote environments where failure is not an option. Pelletier’s major contributions include the development of SkiNet, a Petri net generation tool that enables the formal verification of skillset-based autonomous architectures. He has pioneered predictive runtime verification techniques that can warn operators about actions that would violate system constraints before they occur, effectively allowing autonomous systems to self-monitor in real time. His most cited work, "SkiNet, A Petri Net Generation Tool for the Verification of Skillset-based Autonomous Systems" (2022, 8 citations), established a foundational framework for this approach, while his follow-up paper on predictive runtime verification (2023, 7 citations) extended these methods to online supervision. His most recent work (2025) formalizes the composition of robotic skills into autonomous behaviors. Pelletier’s research bridges the gap between formal verification theory and practical robotic deployment, making him a key figure in the push toward certifiably safe autonomous systems.
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