Papers
2
Total Citations
86
H-Index
2
About
Jory Lafaye is a robotics researcher specializing in motion planning and control systems for humanoid robots, with a particular focus on wheeled locomotion and predictive control methodologies. Their most influential contribution, "Linear Model Predictive Control of the Locomotion of Pepper," published in 2014 and accumulating 67 citations, addressed a significant challenge in humanoid robotics: enabling smooth, efficient locomotion for Pepper, SoftBank Robotics' omnidirectional wheeled platform. This work demonstrated how linear model predictive control (MPC) frameworks could be effectively adapted to the unique kinematic constraints of omnidirectional wheel systems, offering practical solutions for real-world robot deployment. Building on this foundation, Lafaye's 2015 follow-up paper on tilt recovery extended the MPC framework to address balance and stability challenges inherent to wheeled humanoid robots, garnering an additional 19 citations from the international research community. Together, these contributions have meaningfully advanced the field of whole-body control and locomotion planning for service robots, providing methodologies that researchers and engineers continue to reference when developing control architectures for platforms similar to Pepper. Lafaye's work bridges theoretical control engineering and applied humanoid robotics in an accessible and impactful way.
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