Sacha Huriot
Papers
1
Total Citations
2
H-Index
1
About
Sacha Huriot is a rising researcher at the forefront of safe multi-agent robotic systems, with a focus on bridging theoretical guarantees and practical deployment. Their work centers on decentralized control, uncertainty quantification, and safety-critical autonomy—key areas where robots must operate reliably alongside unpredictable agents. Huriot’s major contribution, outlined in their 2025 paper “Safe Decentralized Multi-Agent Control using Black-Box Predictors, Conformal Decision Policies, and Control Barrier Functions,” tackles a fundamental challenge: ensuring safety when robots rely on imperfect black-box models to predict others’ trajectories. By integrating conformal decision theory with control barrier functions, they developed a framework that dynamically adjusts safety constraints based on prediction uncertainty, enabling provably safe yet non-overly conservative behavior in multi-agent settings. Though early in their career, this work has already garnered attention, with 2 citations signaling its relevance to the growing field of learning-enabled control. Huriot’s approach stands out for its theoretical rigor and practical applicability, offering a scalable solution for autonomous driving, drone swarms, and collaborative robotics. As the demand for verifiably safe AI grows, Huriot’s contributions position them as a promising voice in the next wave of control theory research.
Research Focus
Key Achievements
Top Papers
- 1