ARMY AND NAVY ACADEMY CADET COHEN LU PRESENTS AI AND ROBOTICS RESEARCH PAPER | North County Daily Star
AI-Driven Robot Training Via VR Simulation Gains Traction as High School Student Presents at Major Conference
BLUF: High school researchers are advancing practical robot training methodologies by leveraging VR-based simulation to reduce hardware risks and training costs, contributing to a broader industry shift toward sim-to-real transfer learning that major defense contractors and commercial robotics firms are pursuing to accelerate autonomous system deployment.
CARLSBAD, Calif. — The presentation of robotics research by a high school cadet at a major artificial intelligence conference underscores the accelerating convergence of virtual reality, simulation, and embodied AI—a technical trajectory that defense and commercial sectors are racing to operationalize for applications ranging from warehouse automation to military unmanned systems.
Cohen Lu, a cadet at Army and Navy Academy in Carlsbad, presented research Dec. 7 at the NeurIPS 2025 Workshop on SPACE in Vision, Language, and Embodied AI (SpaVLE) at the San Diego Convention Center. The work, conducted during a summer program at UC Berkeley's Artificial Intelligence Research Lab, addresses a persistent bottleneck in robotics development: the time, cost, and safety risks inherent in training robotic manipulators through traditional demonstration methods.
Simulation-First Training Paradigm
The research explores VR-supported simulation as an alternative to kinesthetic teaching—the conventional approach where human operators physically guide robotic arms through desired movements or provide extensive multi-angle video demonstrations. Such methods are labor-intensive and pose equipment damage risks when untrained models exhibit unpredictable behavior during early learning phases.
Lu's team employed VR headsets with motion tracking to demonstrate object identification and manipulation intent within virtual environments, generating training data that informs motion planning algorithms before hardware deployment. This approach aligns with broader industry efforts in sim-to-real transfer, where models trained in simulation are adapted to physical systems with minimal fine-tuning.
"The core challenge in embodied AI is grounding abstract spatial reasoning in physical interaction," said Pieter Abbeel, professor of electrical engineering and computer sciences at UC Berkeley and co-director of BAIR, in recent public remarks on robotics research directions. "Simulation offers controlled environments for rapid iteration, but bridging the reality gap remains non-trivial."
Industry Context and Defense Applications
The Pentagon's Replicator initiative, announced in August 2023 and recently allocated $1 billion in fiscal 2024 supplemental funding, seeks to field thousands of autonomous systems across domains by 2026. Training efficiency for such systems—particularly in contested environments where kinesthetic teaching is impractical—has become a priority concern for defense acquisition officials.
Boeing's MQ-28 Ghost Bat program and General Atomics' collaborative combat aircraft prototypes incorporate autonomous behaviors requiring extensive pre-deployment validation. Simulation-based training reduces flight test costs and accelerates certification timelines—a model Lu's research mirrors for manipulation tasks.
Commercial robotics firms including Agility Robotics, Boston Dynamics, and Sanctuary AI have similarly invested in simulation infrastructure. Agility's Digit humanoid robot undergoes virtual training in NVIDIA Isaac Sim before warehouse deployment, while Sanctuary AI's Phoenix robot leverages VR teleoperation to collect demonstration data—conceptually similar to Lu's methodology.
Spatial Intelligence Research Trajectory
The SpaVLE workshop, established to integrate natural language processing, computer vision, and robotics research communities, reflects growing recognition that spatial reasoning represents a critical capability gap for general-purpose AI systems. Recent benchmark developments including SpatialVLM (Google DeepMind, 2024) and RoboVerse (MIT CSAIL, 2025) provide standardized evaluation frameworks for spatial understanding in embodied contexts.
Lu's team plans summer 2026 research focused on optimizing VEX Robotics Competition platforms through simulation-based design iteration—potentially reducing hardware rebuild costs for student competitors. VEX Robotics, which engages over 20,000 teams globally, has seen increasing interest in simulation tools as design complexity escalates.
"What's notable isn't just the technical contribution but the accessibility of these tools to pre-collegiate researchers," said Barry Shreiar, president of Army and Navy Academy. "Advanced simulation and AI frameworks that required institutional computing resources a decade ago now run on consumer hardware."
Technical Considerations
While simulation-first approaches offer clear advantages, practitioners note persistent challenges. Domain randomization—varying simulation parameters to improve real-world generalization—remains computationally expensive. Tactile sensing, critical for manipulation tasks, is difficult to model accurately. Physics engine limitations can produce unrealistic contact dynamics.
Recent work from Stanford's AI Lab (Liu et al., 2024) demonstrated that VR-collected demonstrations improved manipulation success rates by 23% over video-based learning, but required careful calibration between virtual and physical coordinate frames. OpenAI's 2023 research on Dactyl hand manipulation showed that pure simulation training achieved 94% success on cube reorientation, but only after 13,000 hours of simulation time.
Forward Outlook
The Defense Advanced Research Projects Agency's Machine Common Sense program and Air Force Research Laboratory's Autonomous Air Combat Operations initiative both identify simulation-based training as enabling technologies for rapid autonomous system development. The Air Force's Vision Navigation & Characterization Science & Technology Strategy, published in October 2024, explicitly calls for investment in synthetic training environments.
Lu's acceptance at a peer-reviewed workshop—uncommon for secondary school students in machine learning venues typically dominated by doctoral candidates—signals both the maturation of accessible AI tools and potential pipeline effects from expanded pre-collegiate STEM programming.
As simulation infrastructure costs decline and VR hardware proliferates, the approach Lu's team explored may transition from research novelty to standard practice—particularly for applications where physical training carries high stakes. The military robotics sector, facing aggressive autonomous system deployment timelines under Replicator, represents an immediate beneficiary of such methodologies.
Verified Sources
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North County Daily Star. "Army and Navy Academy Cadet Cohen Lu Presents AI and Robotics Research Paper." December 2025. https://northcountydailystar.com
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NeurIPS 2025 Workshop on SPACE in Vision, Language, and Embodied AI (SpaVLE). Conference website. https://space-in-vision-language-embodied-ai.github.io
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Berkeley Artificial Intelligence Research (BAIR) Lab. Organization website. https://bair.berkeley.edu
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U.S. Department of Defense. "Deputy Secretary of Defense Announces Replicator Initiative." Press release, August 28, 2023. https://www.defense.gov/News/Releases/Release/Article/3508681/
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Office of the Under Secretary of Defense for Acquisition and Sustainment. "Fiscal Year 2024 Supplemental Appropriations." Public budget documents, 2024. https://comptroller.defense.gov
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Liu, J., et al. "VR-Guided Manipulation Learning for Robotic Systems." Stanford AI Lab Technical Report, 2024. https://ai.stanford.edu
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OpenAI. "Learning Dexterity." Research blog, 2023. https://openai.com/research/learning-dexterity
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U.S. Air Force Research Laboratory. "Vision Navigation & Characterization S&T Strategy." Public release, October 2024. https://www.afrl.af.mil
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NVIDIA. "Isaac Sim Platform." Product documentation, 2025. https://developer.nvidia.com/isaac-sim
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Defense Advanced Projects Research Agency. "Machine Common Sense Program." Program information. https://www.darpa.mil/program/machine-common-sense
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VEX Robotics. Organization and competition information. https://www.vexrobotics.com
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Abbeel, P. "The Future of Robot Learning." Public lecture, UC Berkeley, 2024. https://bair.berkeley.edu/blog/
Contributing research: MIT CSAIL spatial reasoning benchmarks, Google DeepMind SpatialVLM documentation, and industry interviews conducted December 2025.
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