John Alora

Contacts:

Email: jjalora at stanford dot edu

John Alora

John is a graduate student in the Department of Aeronautics and Astronautics at Stanford University. He completed his B.S. degree in electrical engineering at the United States Air Force Academy (USAFA) in 2014 and subsequently, his M.S. degree in Aeronautics and Astronautics at MIT in 2016 under the supervision of Professor Sertac Karaman. He was also a Draper Fellow at Draper Labs working on implementing task and motion planning algorithms for UAVs operating in contested environments. He is currently supported by the Secretary of the Air Force STEM PhD Fellowship.

John’s research interests lie at the intersection of robotics, control theory, and optimization. His current work involves development of novel physics-based machine learning techniques for control of infinite-dimensional systems, with applications to soft robots and autonomous aircraft.

John is an Air Force pilot with over 1,000 flight hours; prior to Stanford, he flew the B-52H and deployed across the globe in support of the President’s strategic objectives.

Awards:

Secretary of the Air Force STEM Fellowship (2021)
Draper Fellowship (2014-2016)
Distinguished Graduate (Top 10%), USAFA

ASL Publications

M. Bazzi, A. Shahid, C. Agia, J. Alora, M. Forgione, D. Piga, F. Braghin, M. Pavone, and L. Roveda, “RoboMorph: In-Context Meta-Learning for Robot Dynamics Modeling,” in Proc. of the IFAC/AAAI Int. Conf. on Informatics in Control, Automation and Robotics, Porto, Portugal, 2024.
[BibTeX] [Abstract]

Abstract: The landscape of Deep Learning has experienced a major shift with the pervasive adoption of Transformer-based architectures, particularly in Natural Language Processing (NLP). Novel avenues for physical applications, such as solving Partial Differential Equations and Image Vision, have been explored. However, in challenging domains like robotics, where high non-linearity poses significant challenges, Transformer-based applications are scarce. While Transformers have been used to provide robots with knowledge about high-level tasks, few efforts have been made to perform system identification. This paper proposes a novel methodology to learn a meta-dynamical model of a high-dimensional physical system, such as the Franka robotic arm, using a Transformer-based architecture without prior knowledge of the system’s physical parameters. The objective is to predict quantities of interest (end-effector pose and joint positions) given the torque signals for each joint. This prediction can be useful as a component for Deep Model Predictive Control frameworks in robotics. The meta-model establishes the correlation between torques and positions and predicts the output for the complete trajectory. This work provides empirical evidence of the efficacy of the in-context learning paradigm, suggesting future improvements in learning the dynamics of robotic systems without explicit knowledge of physical parameters. Code, videos, and supplementary materials can be found at project website. See this https://sites.google.com/view/robomorph.
```
@inproceedings{BazziShahidEtAl2024,
  author = {Bazzi, M. and Shahid, A. and Agia, C. and Alora, J. and Forgione, M. and Piga, D. and Braghin, F. and Pavone, M. and Roveda, L.},
  title = {RoboMorph: In-Context Meta-Learning for Robot Dynamics Modeling},
  booktitle = {{Proc. of the IFAC/AAAI Int. Conf. on Informatics in Control, Automation and Robotics}},
  year = {2024},
  month = aug,
  address = {Porto, Portugal},
  owner = {agia},
  timestamp = {2024-10-30},
  url = {https://arxiv.org/abs/2409.11815}
}
```
L. Pabon, J. Köhler, J. I. Alora, P. B. Eberhard, A. Carron, M. N. Zeilinger, and M. Pavone, “Perfecting Periodic Trajectory Tracking: Model Predictive Control with a Periodic Observer,” in IEEE/RSJ Int. Conf. on Intelligent Robots & Systems, Abu Dhabi, 2024.
[BibTeX] [Abstract]

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.
```
@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 = {{IEEE/RSJ Int. Conf. on Intelligent Robots \& Systems}},
  address = {Abu Dhabi},
  url = {https://arxiv.org/abs/2404.01550},
  owner = {lpabon},
  timestamp = {2024-07-01}
}
```
J. I. Alora, M. Cenedese, E. Schmerling, G. Haller, and M. Pavone, “Practical Deployment of Spectral Submanifold Reduction for Optimal Control of High-Dimensional Systems,” in IFAC World Congress, Yokohama, Japan, 2023.
[BibTeX] [Abstract]

Abstract: Real-time optimal control of high-dimensional, nonlinear systems remains a challenging task due to the computational intractability of their models. While several model-reduction and learning-based approaches for constructing low-dimensional surrogates of the original system have been proposed in the literature, these approaches suffer from fundamental issues which limit their application in real-world scenarios. Namely, they typically lack generalizability to different control tasks, ability to trade dimensionality for accuracy, and ability to preserve the structure of the dynamics. Recently, we proposed to extract low-dimensional dynamics on Spectral Submanifolds (SSMs) to overcome these issues and validated our approach in a highly accurate simulation environment. In this manuscript, we extend our framework to a real-world setting by employing time-delay embeddings to embed SSMs in an observable space of appropriate dimension. This allows us to learn highly accurate, low-dimensional dynamics purely from observational data. We show that these innovations extend Spectral Submanifold Reduction (SSMR) to real-world applications and showcase the effectiveness of SSMR on a soft robotic system.
```
@inproceedings{AloraCenedeseEtAl2023b,
  author = {Alora, J.I. and Cenedese, M. and Schmerling, E. and Haller, G. and Pavone, M.},
  title = {Practical Deployment of Spectral Submanifold Reduction for Optimal Control of High-Dimensional Systems},
  booktitle = {{IFAC World Congress}},
  year = {2023},
  address = {Yokohama, Japan},
  month = jul,
  doi = {10.1016/j.ifacol.2023.10.1734},
  owner = {jthluke},
  timestamp = {2024-09-20},
  url = {/wp-content/papercite-data/pdf/Alora.Cenedese.IFAC23.pdf}
}
```
J. I. Alora, L. Pabon, J. Köhler, M. Cenedese, E. Schmerling, Z. M. N., G. Haller, and M. Pavone, “Robust Nonlinear Reduced-Order Model Predictive Control,” in Proc. IEEE Conf. on Decision and Control, Singapore, 2023.
[BibTeX] [Abstract]

Abstract: Real-world systems are often characterized by high-dimensional nonlinear dynamics, making them challenging to control in real time. While reduced-order models (ROMs) are frequently employed in model-based control schemes, dimensionality reduction introduces model uncertainty which can potentially compromise the stability and safety of the original high-dimensional system. In this work, we propose a novel reduced-order model predictive control (ROMPC) scheme to solve constrained optimal control problems for nonlinear, high-dimensional systems. To address the challenges of using ROMs in predictive control schemes, we derive an error bounding system that dynamically accounts for model reduction error. Using these bounds, we design a robust MPC scheme that ensures robust constraint satisfaction, recursive feasibility, and asymptotic stability. We demonstrate the effectiveness of our proposed method in simulations on a high-dimensional soft robot with nearly 10,000 states.
```
@inproceedings{AloraPabonEtAl2023,
  author = {Alora, J.I. and Pabon, L. and Köhler, J. and Cenedese, M. and Schmerling, E. and N., Zeilinger M. and Haller, G. and Pavone, M.},
  title = {Robust Nonlinear Reduced-Order Model Predictive Control},
  year = {2023},
  keywords = {pub},
  booktitle = {{Proc. IEEE Conf. on Decision and Control}},
  address = {Singapore},
  url = {https://arxiv.org/abs/2309.05746},
  owner = {jjalora},
  timestamp = {2023-09-11}
}
```
J. I. Alora, M. Cenedese, E. Schmerling, G. Haller, and M. Pavone, “Data-Driven Spectral Submanifold Reduction for Nonlinear Optimal Control of High-Dimensional Robots,” in Proc. IEEE Conf. on Robotics and Automation, London, United Kingdom, 2023.
[BibTeX] [Abstract]

Abstract: Modeling and control of high-dimensional, nonlinear robotic systems remains a challenging task. While various model- and learning-based approaches have been proposed to address these challenges, they broadly lack generalizability to different control tasks and rarely preserve the structure of the dynamics. In this work, we propose a new, data-driven approach for extracting low-dimensional models from data using Spectral Submanifold Reduction (SSMR). In contrast to other data-driven methods which fit dynamical models to training trajectories, we identify the dynamics on generic, low-dimensional attractors embedded in the full phase space of the robotic system. This allows us to obtain computationally-tractable models for control which preserve the system’s dominant dynamics and better track trajectories radically different from the training data. We demonstrate the superior performance and generalizability of SSMR in dynamic trajectory tracking tasks vis-a-vis the state of the art.
```
@inproceedings{AloraCenedeseEtAl2023,
  author = {Alora, J.I. and Cenedese, M. and Schmerling, E. and Haller, G. and Pavone, M.},
  booktitle = {{Proc. IEEE Conf. on Robotics and Automation}},
  title = {Data-Driven Spectral Submanifold Reduction for Nonlinear Optimal Control of High-Dimensional Robots},
  year = {2023},
  address = {London, United Kingdom},
  doi = {10.1109/ICRA48891.2023.10160418},
  owner = {somrita},
  timestamp = {2024-02-29},
  url = {https://arxiv.org/abs/2209.05712}
}
```
F. Mahlknecht, J. I. Alora, S. Jain, E. Schmerling, R. Bonalli, G. Haller, and M. Pavone, “Using Spectral Submanifolds for Nonlinear Periodic Control,” in Proc. IEEE Conf. on Decision and Control, 2022.
[BibTeX] [Abstract]

Abstract: Very high dimensional nonlinear systems arise in many engineering problems due to semi-discretization of the governing partial differential equations, e.g. through finite element methods. The complexity of these systems present computational challenges for direct application to automatic control. While model reduction has seen ubiquitous applications in control, the use of nonlinear model reduction methods in this setting remains difficult. The problem lies in preserving the structure of the nonlinear dynamics in the reduced order model for high-fidelity control. In this work, we leverage recent advances in Spectral Submanifold (SSM) theory to enable model reduction under well-defined assumptions for the purpose of efficiently synthesizing feedback controllers.
```
@inproceedings{MahlknechtAloraEtAl2022,
  author = {Mahlknecht, F. and Alora, J.I. and Jain, S. and Schmerling, E. and Bonalli, R. and Haller, G. and Pavone, M.},
  booktitle = {{Proc. IEEE Conf. on Decision and Control}},
  title = {Using Spectral Submanifolds for Nonlinear Periodic Control},
  year = {2022},
  keywords = {pub},
  owner = {jjalora},
  timestamp = {2022-11-22},
  url = {https://arxiv.org/abs/2209.06573}
}
```