Adding hugo (#81)

* Create hugo-buurmeijer.markdown

* Add files via upload

* Update visiting.markdown

* Update luis-pabon.markdown

* Update ASL_Bib.bib

* Update robin-brown.markdown

* Update robert-dyro.markdown

* Update alvin-sun.markdown

_bibliography/ASL_Bib.bib

@@ -5121,6 +5121,18 @@ @inproceedings{ArenaFortunaEtAl2005
   url       = {/wp-content/papercite-data/pdf/Arena.Fortuna.ea.ISCAS05.pdf}
 }
 
+@inproceedings{PabonEtAl2024,
+  author    = {Pabon, L. and Köhler, J. and Alora, J.I. and Eberhard, P.B. and Carron, A. and Zeilinger, M.N. and Pavone, M.},
+  title     = {Perfecting Periodic Trajectory Tracking: Model Predictive Control with a Periodic Observer},
+  year      = {2024},
+  booktitle = proc_IEEE_IROS,
+  address   = {Abu Dhabi},
+  abstract  = {In Model Predictive Control (MPC), discrepancies between the actual system and the predictive model can lead to substantial tracking errors and significantly degrade performance and reliability. While such discrepancies can be alleviated with more complex models, this often complicates controller design and implementation. By leveraging the fact that many trajectories of interest are periodic, we show that perfect tracking is possible when incorporating a simple observer that estimates and compensates for periodic disturbances. We present the design of the observer and the accompanying tracking MPC scheme, proving that their combination achieves zero tracking error asymptotically, regardless of the complexity of the unmodelled dynamics. We validate the effectiveness of our method, demonstrating asymptotically perfect tracking on a high-dimensional soft robot with nearly 10,000 states and a fivefold reduction in tracking errors compared to a baseline MPC on small-scale autonomous race car experiments.},
+  url       = {https://arxiv.org/abs/2404.01550},
+  owner     = {lpabon},
+  timestamp = {2024-07-01}
+}
+
 @inproceedings{AloraPabonEtAl2023,
   author    = {Alora, J.I. and Pabon, L. and Köhler, J., and Cenedese, M. and Schmerling, E. and Zeilinger M. N. and Haller, G. and Pavone, M.},
   title     = {Robust Nonlinear Reduced-Order Model Predictive Control},

_people/alvin-sun.markdown

@@ -3,8 +3,9 @@ layout: person
 title:  "Yixiao (Alvin) Sun"
 last:   "Sun"
 date:   2022-03-31 00:00:00 -0700
-position: phd
+position: alumni
 excerpt: "Uncertainty calibration for deep models"
+current: "Skydio"
 img: AlvinSun.jpg
 email: [email protected]
 github: alvinsunyixiao

_people/hugo-buurmeijer.markdown

@@ -0,0 +1,19 @@
+---
+layout: person
+title: “Hugo Buurmeijer”
+last:  “Buurmeijer”
+date:  2024-06-22 00:00:00
+position: phd
+excerpt: “Optimal and learning-based control of high-dimensional robots”
+img: hbuurmei.jpeg
+email: [email protected]
+github: hbuurmei
+linkedin: hbuurmei
+website: https://hbuurmei.github.io
+gscholar: ywP7WbMAAAAJ
+---
+Hugo is a PhD student in the Department of Aeronautics and Astronautics at Stanford University. He completed his BSc degree in Aerospace Engineering at Delft University of Technology in 2022 and his MSc in Aeronautics and Astronautics at Stanford University in 2024. Additionally, he held a research fellow position at Harvard University in the Computational Robotics group directed by professor Heng Yang in 2023.
+Hugo’s research currently focuses on the control of high-dimensional robotics systems, leveraging both optimal control and novel learning-based architectures, with applications in bio-inspired and soft robotics. He is further interested in autonomy for space missions, and safety analysis of learning-based modules.
+In his free time, Hugo enjoys watching and playing soccer, reading and traveling.
+### Awards:
+- Stanford Graduate Fellowship, 2024

_people/luis-pabon.markdown

@@ -9,7 +9,7 @@ img: LuisPabon.jpg
 email: [email protected]
 linkedin: luis-p
 website: https://luispabon.info/
-# gscholar:
+gscholar: cjt9tVkAAAAJ
 ---
 
 Luis is a Ph.D. student in the Department of Aeronautics and Astronautics. His research lies at the intersection of machine learning and control theory, targeting advancements in robotics and autonomous systems. Luis focuses on developing algorithms that enable robots to efficiently learn from their own experience, safely interact with the world around them, and adapt to unfamiliar environments.

_people/robert-dyro.markdown

@@ -3,8 +3,9 @@ layout: person
 title:  "Robert Dyro"
 last:   "Dyro"
 date:   2020-03-19 00:00:00 -0700
-position: phd
+position: alumni
 excerpt: "Structured modeling and optimal planning for robotics"
+current: Google
 img: RobertDyro.jpg
 email: [email protected]
 website: https://rdyro.github.io/website/

_people/robin-brown.markdown

@@ -3,8 +3,9 @@ layout: person
 title:  "Robin Brown"
 last:   "Brown"
 date:   2020-03-19 00:00:00 -0700
-position: phd
+position: alumni
 excerpt: "Scalable algorithms for multi-agents systems"
+current: "OpenAI"
 img: RobinBrown.jpg
 email: [email protected]
 ---

_people/visiting.markdown

@@ -10,6 +10,7 @@ position: visiting
 <li><h6 class="visiting-name">Davide Celestini.</h6> <p class="visiting-excerpt">Politecnico di Torino.</p></li>
 <li><h6 class="visiting-name">Claudia Conte.</h6> <p class="visiting-excerpt">University of Naples Federico II.</p></li>
 <li><h6 class="visiting-name">Roman Engeler.</h6> <p class="visiting-excerpt">ETH Zürich.</p></li>
+<li><h6 class="visiting-name">Zhe Fu.</h6> <p class="visiting-excerpt">University of California, Berkeley.</p></li>
 <li><h6 class="visiting-name">Javier V. Gómez.</h6> <p class="visiting-excerpt">Universidad Carlos III de Madrid.</p></li>
 <li><h6 class="visiting-name">Alvaro Estandia Hentschel.</h6> <p class="visiting-excerpt">ETH Zürich.</p></li>
 <li><h6 class="visiting-name">Dominik Helmreich.</h6> <p class="visiting-excerpt">ETH Zürich.</p></li>