Jeffrey Aguilar

About

Jeffrey Aguilar is a leading figure in the emerging field of locomotion robophysics, where he integrates robotics, soft matter physics, and dynamical systems to uncover the fundamental principles governing movement. His highly cited 2016 review (284 citations) established a foundational framework for this interdisciplinary approach, demonstrating how robotic models can reveal the physical laws that both enable and constrain effective self-propulsion across diverse environments. Aguilar’s experimental work has produced major contributions to understanding locomotion on complex, deformable terrain. His 2015 study on jumping dynamics in granular media (125 citations) and his 2012 analysis of lift-off dynamics in a simple jumping robot (30 citations) revealed counterintuitive optimal performance regimes, showing that peak jumping height occurs away from a system’s natural resonance. More recently, his 2020 paper on learning terrain dynamics (23 citations) pioneered the use of Gaussian process modeling for real-time adaptation, enabling robots to autonomously adjust their jumping strategies on uncharacterized, non-rigid surfaces. Through this combination of theoretical insight and practical control frameworks, Aguilar has established himself as a key innovator in the quest for more agile, terrain-aware robotic locomotion.

Research Focus

Key Achievements

Top Papers

1. 1
   A review on locomotion robophysics: the study of movement at the intersection of robotics, soft matter and dynamical systems

   284 citations · 2016
2. 2
   Robophysical study of jumping dynamics on granular media

   125 citations · 2015
3. 3
   Lift-Off Dynamics in a Simple Jumping Robot

   30 citations · 2012
4. 4
   Learning Terrain Dynamics: A Gaussian Process Modeling and Optimal Control Adaptation Framework Applied to Robotic Jumping

   23 citations · 2020

Key Collaborators

Contact & Links