Machine Learning

VSCL Students Present at 2026 AIAA SciTech Forum

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VSCL researchers Raul Santos, Seth Johnson, Carla Zaramella, Zach Curtis will present papers in January at the 2026 AIAA SciTech Forum in Orlando, Florida.

On 12 January, Raul Santos will present the paper ”Deep Reinforcement Learning Waypoint Generation for Attitude Station-Keeping with Sun Avoidance”. This work studies deep reinforcement learning–based waypoint generation for autonomous on-orbit attitude control and examines how observation and action space design influence neural network performance.

Santos, Raul, Binz, Sadie, McQuinn,Cassie-Kay, Valasek, John, Hamilton, Nathaniel, Hobbs, Kerianne L., and Dulap, Kyle, ”Deep Reinforcement Learning Waypoint Generation for Attitude Station-Keeping with Sun Avoidance,” 2026 AIAA Science and Technology Forum and Exposition, Orlando, FL, 12 January 2026

 

On 12 January, Seth Johnson will present the paper “Modular Open System Architecture for Low-cost Integrated Avionics (MOSA LINA)”. This work investigates a modular, open-system avionics architecture for experimental vehicles that reduces integration complexity and supports platform-agnostic mission reconfiguration through plug-and-play sensor integration. Two case studies are investigated: one focused on synchronized high-fidelity data collection and the other on autonomous fixed-wing target tracking.

Johnson, Seth, Santos, Raul, Martinez-Banda, Isabella, Luna, Noah, and Valasek, John, “Modular Open System Architecture for Low-cost Integrated Avionics (MOSA LINA),” 2026 AIAA Science and Technology Forum and Exposition, Orlando, FL, 12 January 2026.

 

On 15 January, Carla Zaramella will present the paper “Identification of Non-Dimensional Aerodynamic Derivatives using Markov Parameter Based Least Squares Identification Algorithm”. This work expands apon previous developments of the MARBLES algorithm to directly identify non-dimensional stability and control derivatives using computed Markov Parameters with a least squares estimator and a priori information.

Leshikar, Christopher, Zaramella, Carla, Madewell, Evelyn, and Valasek, John, “Identification of Non-Dimensional Aerodynamic Derivatives using Markov Parameter Based Least Squares Identification Algorithm,” 2026 AIAA Science and Technology Forum and Exposition, Orlando, FL, 15 January 2026

 

On 16 January, Zachary Curtis will present the paper “Real-Time Controller Architecture for sUAS Flight Test”. This work investigates a C++/ROS architecture for real -time controller implementation. The said architecture, Kanan, allows safe and fast integration of custom controllers across a broad range of vehicles and controller types.

Luna, Noah, Valasek, John, and Curtis, Zachary, “Real-Time Controller Architecture for sUAS Flight Test,”  2026 AIAA Science and Technology Forum and Exposition, Orlando, FL, 16 January 2026.

 

Payton Clem Defends Masters Thesis

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Payton Clem successfully defended her M.S. thesis Autonomous Target Tracking of Hostile Ground Target under Wind Disturbance and Sun Concealment using Deep Reinforcement Learning on 12 December.  Payton has been with VSCL since the first semester of her senior year and is highly engaged in AI and flight testing.

Intelligence, surveillance, and reconnaissance (ISR) missions benefit from the use of unmanned aircraft systems (UAS) capable of maintaining visual contact with ground targets, referred to here as target tracking. For practical deployment, it is valuable for tracking to be autonomous and function without detailed knowledge of the surrounding environment. The task becomes more complex when additional objectives, such as concealment or avoiding a hostile target, are introduced. To address this problem, a Soft Actor-Critic (SAC) reinforcement learning controller is developed that uses only the target’s location in the image frame. The agent controls a multirotor UAS equipped with a fixed optical sensor, requiring the agent to adjust vehicle attitude to keep the target in view while accounting for wind, varying target behaviors, altitude-based concealment constraints, and sun-related concealment. Previous work on fixed-camera target tracking has shown that RL-based algorithms can produce unstable behaviors such as control oscillations and large altitude changes. This work focuses on reward shaping to mitigate these issues and encourage stable, consistent tracking. In addition, the influence of including solar concealment information in the reward function is examined to assess its effect on vehicle behavior. The results demonstrate that the proposed reward structure effectively reduces unwanted behaviors such as diving and pitch and yaw ringing. The reward structure enables stable, long-duration tracking, despite the incorporation of constraints associated with sun concealment strategies. The resulting policy achieves reliable tracking across the evaluated conditions.

Payton’s research is supported by the Army Research Laboratory on the project Robust Threat Detection for Ground Combat Vehicles with Multi-Domain Surveillance in Hostile Environments

Hannah Lehman Defends Ph.D. Dissertation

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Hannah Lehman successfully defended her Ph.D. dissertation on May 27th, 2025.  Hannah has been with VSCL since her freshman year in Spring 2017, for a total of 8.42 years with VSCL during which she implemented the Theory-Computation-Experiment paradigm. The title of her dissertation is: Hierarchical Auctions for the Coordination of Heterogeneous Agents using Machine Learning

Hannah’s dissertation investigates autonomous multiagent coordination.  Machine learning has long been discussed as a candidate for facilitating autonomous multiagent vehicle coordination. Many methods of autonomous multiagent coordination have been proposed, however few if any solutions consider realistic communication challenges. By using machine learning on multiple levels, and a self organizing hierarchical system, an autonomous, pseudo decentralized, heterogeneous, system can dynamically complete tasks without being fully connected. This approach, called Hierarchical Auctions for the Coordination of Heterogeneous Agents (HACHA) will be investigated and demonstrated on four simple, proof of concept simulations. Each simulation scenario is designed to demonstrate HACHA’s applicability to a different subset of multiagent problems and address specific requirements. Within HACHA, specific algorithm and data choices will be motivated real-world hardware constraints and informed by time complexity analysis of sub-algorithms. Results show that a parallel auction coordination framework can be used to organize multiple heterogeneous agents with different sensors, movement modalities, graph connectedness, and controllers to complete a task requiring multiple agents. The auction framework is independent of individual agents and has been utilized in this paper by a combination of reinforcement learning trained agents and optimally controlled agents to complete tasks. HACHA auction propagation methods are explored and recommended use case rules are developed based on theoretical and computational investigations and results. The HACHA auction choice is explored and compared to other popular auction methods over a variety of relevant network characteristics including dynamicism, sparsity, and number of tasks.

Hannah will be doing a short postdoc with VSCL and then starting full-time at Sandia National Laboratories, where she has now done four graduate internships, in July. Hannah’s is the 62nd graduate degree advised by Dr. John Valasek and the 15th Ph.D. student.

Dr. John Valasek Reaches Career Milestone

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In October Dr. John Valasek reached a career milestone by presenting at his 100th invited seminar/lecture/panelist.

Chronologically:

#1 “Fighter Agility Metrics, Research, and Test,” Lockheed Advanced Development Projects Division (Skunk Works), Burbank, CA, 13 July 1990.

#100 “Multiple-Time-Scale Nonlinear Output Feedback Control of Systems With Model Uncertainties,” Department of Aerospace Engineering, University of Maryland, College Park, MD, 9 October 2024.

Congratulations Dr. Valasek!

MD Sunbeam Defends Masters Thesis

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MD Sunbeam (B.S. Aerospace Engineering, University of Texas) successfully defended his Masters thesis: “Gaze-Regularized Imitation Learning”. 

Approaches for teaching learning agents via human demonstrations have been widely studied and successfully applied to multiple domains. However, the majority of imitation learning work utilizes only behavioral information from the demonstrator, i.e. which actions were taken, and ignores other useful information. In particular, eye gaze information can give valuable insight towards where the demonstrator is allocating visual attention, and holds the potential to improve agent performance and generalization. In this work, we propose Gaze Regularized Imitation Learning (GRIL), a novel context-aware, imitation learning architecture that learns concurrently from both human demonstrations and eye gaze to solve tasks where visual attention provides important context. We apply GRIL to a visual navigation task, in which an unmanned quadrotor is trained to search for and navigate to a target vehicle in a photo-realistic simulated environment. We show that GRIL outperforms several state-of-the-art gaze-based imitation learning algorithms, simultaneously learns to predict human visual attention, and generalizes to scenarios not present in the training data.

This work is sponsored by the Army Research Laboratory (ARL) through the Cycle of Learning Project. MD Sunbeam is employed as a researcher at the Human Research and Engineering Directorate (HRED), ARL.

Krpec and Valasek Publish “Vision-based Marker-less Landing of a UAS on Moving Ground Vehicle” in Journal of Aerospace Information Systems

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VSCL Alumni Blake Krpec, Dr. John Valasek, and Dr. Stephen Nogar with the DEVCOM Army Research Lab published the paper “Vision-based Marker-less Landing of a UAS on Moving Ground Vehicle” in Journal of Aerospace Information Systems.

Current autonomous unmanned aerial systems (UAS) commonly use vision-based landing solutions that depend upon fiducial markers to localize a static or mobile landing target relative to the UAS. This paper develops and demonstrates an alternative method to fiducial markers with a combination of neural network-based object detection and camera intrinsic properties to localize an unmanned ground vehicle (UGV) and enable autonomous landing. Implementing this visual approach is challenging given the limited compute power on board the UAS, but is relevant for autonomous landings on targets for which affixing a fiducial marker a priori is not possible, or not practical. The position estimate of the UGV is used to formulate a landing trajectory that is then input to the flight controller. Algorithms are tailored towards low size, weight, and power constraints as all compute and sensing components weigh less than 100 g. Landings were successfully demonstrated in both simulation and experimentally on a UGV traveling in both a straight line and while turning. Simulation landings were successful at UGV speeds of up to 3.0 m/s, and experimental landings at speeds up to 1.0 m/s.

 

Texas A&M University Becomes Founding Partner of New NSF Center for Autonomous Air Mobility and Sensing

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Texas A&M University is a founding partner of the National Science Foundation (NSF) Center for Autonomous Air Mobility and Sensing (CAAMS) along withUniversity of Colorado Boulder (CU), Brigham Young University (BYU), University of Michigan (UM), Penn State University (PSU), and Virginia Tech (VT). The center is organized under the NSF’s Industry-University Cooperative Research Centers program (IUCRC). CAAMS consists of three primary partners: academia, industry, and government. Academic faculty collaborate with industry and government members to promote long-term global competitive research and innovation. They create solutions to the most critical challenges faced in the autonomous industry. Dr. John Valasek serves as the Site Director for Texas A&M University. Texas A&M University faculty associated with CAAMS include: Dr. Moble Benedict, Dr. Manoranjan Majji, Dr. Sivakumar Rathinem, and Dr. Swaroop Darbha.

In conjunction with the CASS Lab at Penn State, directed by Dr. Puneet Singla, VSCL will be working on the project Integration of System Theory with Machine Learning Tools for Data Driven System Identification.  This project integrates system theory with machine learning tools for data driven system identification. The objective is to derive nonlinear dynamical models by employing a unique handshake between linear time varying subspace methods and sparse approximation tools from high fidelity flight simulations and flight experiments.

 

VSCL Student Presents at Interactive Learning with Implicit Human Feedback Workshop at 2023 International Conference on Machine Learning (ICML)

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VSCL graduate student M.D. Sunbeam will present a workshop paper on 29 July at the 2023 International Conference on Machine Learning (ICML) in Honolulu, Hawaii.

Sunbeam will be presenting the paper “Imitation Learning with Human Eye Gaze via Multi-Objective Prediction,”. Approaches for teaching learning agents via human demonstrations have been widely studied and successfully applied to multiple domains. However, the majority of imitation learning work utilizes only behavioral information from the demonstrator, i.e. which actions were taken, and ignores other useful information. In particular, eye gaze information can give valuable insight towards where the demonstrator is allocating visual attention, and holds the potential to improve agent performance and generalization. In this work, we propose Gaze Regularized Imitation Learning (GRIL), a novel context-aware, imitation learning architecture that learns concurrently from both human demonstrations and eye gaze to solve tasks where visual attention provides important context.

We apply GRIL to a visual navigation task, in which an unmanned quadrotor is trained to search for and navigate to a target vehicle in a photorealistic simulated environment. We show that GRIL outperforms several state-of-the-art gaze-based imitation learning algorithms, simultaneously learns to predict human visual attention, and generalizes to scenarios not present in the training data. Supplemental videos can be found at https://sites.google.com/view/gaze-regularized-il/, and code will be made available.

VSCL Student Blake Krpec Defends Master of Science Thesis

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VSCL student Blake Krpec, who will graduate with his Master of Science degree in May 2022, has defended his thesis “Vision-Based Marker-Less Landing of a UAS On a Moving Ground Vehicle”.  Blake’s defense had 34 people in attendance including many in attendance from the Army Research Lab.  His committee is Drs. Reza Langari, Manoranjan Majji, Srikanth Saripalli, and Stephen Nogar (special committee member from Army Research Laboratory and the Technical Monitor). His research is supported as a Journeyman Fellow by the Army Research Laboratory (ARL) on an Oak Ridge Associated Universities (ORAU) Fellowship. Blake has been working with VSCL for 7 years after joining as a freshman. After graduation, Blake will work with Southwest Research Institute in San Antonio, TX.

VSCL student Ritwik Bera Defends Master of Science Thesis

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VSCL student Ritwik Bera, who will graduate with his Master of Science degree in May 2022, has defended his thesis “A Modular Framework for Training Autonomous Systems via Human Interaction”.  Bera joined VSCL in 2019 after having spent a summer working with the lab in 2017. Bera previously participated in summer a internships at Zoox in Summer 2021. After graduation, Bera will work with Zoox in Foster CIty, CA as a Software Engineer in the Planning and Control department working on trajectory generation algorithms.