SE

Zhixin(Sean) Li

@Sean-ZhiXin-Li

Exploring AI, robotics, and space systems through self-directed projects and reproducible research.

Joined September 2024

3 Followers

20 Following

AI Insights & Achievements

23/ 100

Consistency

45 day streak.

Profile README

Hi, I'm Sean (Zhixin Li)

High school student · Self-directed researcher · AI × Spacecraft Control

♾️ Mission & Identity

I am fascinated by how a spacecraft survives in silence —
and how intelligence, embedded in software, might become its second heartbeat.

I explore this question by building reproducible simulations, controllers, and logs,
treating learning itself as an engineering process rather than a competition.

Trajectories drift. Controllers adapt.
Failure is telemetry.

🛰️ Flagship Project — Spacecraft AI Controller

A long-term, self-directed research project focused on
AI-controlled orbital propulsion systems in simulation.

This project is not a demo.
It is an evolving system designed with engineering rigor and traceability.

Core Focus

Physics-based orbital dynamics and propulsion modeling
Expert (rule-based) controllers as stable baselines
Transition to imitation learning and reinforcement learning
Decision-making over:
- thrust ignition
- thrust magnitude
- impulse vs continuous propulsion
Long-horizon rollouts under:
- noisy inputs
- partial actuator faults
- imperfect state information

Engineering Principles

Every run produces logs treated as mission data
Results are reproducible and auditable
Failures are recorded, categorized, and analyzed
Progress is measured by stability and insight, not only reward curves

This work produced my first reproducible orbital transfer simulation
and a stabilized expert baseline, validated across multiple orbital conditions.

🔗 https://github.com/Sean-ZhiXin-Li/spacecraft-ai-controller

🛠️ Tech Foundations

Every autonomous system stands on layers of engineering.

Alongside my main project, I build foundational skills across disciplines that support spacecraft autonomy:

🤖 Robotics and control experiments
⚡ Embedded systems (Arduino, sensors, low-level interfaces)
🛰️ CubeSat structural modeling and CAD exploration
🎮 Reinforcement learning from simple environments to orbital-scale tasks

🔗 https://github.com/Sean-ZhiXin-Li/tech-foundations

🧪 Engineering Training Ground — engineering_ai_playground

A process-focused engineering repository designed to build
long-term, lab-ready capability, not showcase results.

This repository is not a research project and not a product.
It exists to answer one question clearly:

Can I independently build, debug, and iterate on a Python + AI engineering system
in a Linux environment, from a blank file?

What This Repo Emphasizes

Python engineering from scratch (no templates)
Linux / WSL–based workflow
Git discipline and reproducibility
Debugging as a first-class skill
Clear execution → metrics → verification pipeline

Each run leaves persistent, inspectable traces.
Failures are preserved, categorized, and verified — never hidden.

This repository supports future research work by strengthening
engineering habits that scale to collaborative lab environments.

🔗 https://github.com/Sean-ZhiXin-Li/engineering_ai_playground

📓 Research Philosophy

For me, research is not only about results.
It is about process integrity.

Commits are signals: proof that the system is still alive
Bugs are disturbances that reveal structure
Logs are navigation charts through uncertainty
Progress is often spiral, not linear

Traditional drills and competitions exhausted me.
Open-ended projects taught me persistence.

Even when today’s trajectory is unclear,
the learning system continues to integrate.

🌌 Curiosity & Direction

I am curious about the universe —
about regions no probe has reached and signals that have gone silent.

Each lost spacecraft feels like a failure of endurance.
That feeling pushes me to design controllers that adapt longer,
degrade gracefully, and survive uncertainty.

I do not attempt to master all of physics or AI.
I focus only on what matters for spacecraft autonomy,
assembling knowledge module by module.

⏳ Timeline (Evolving)

This is not a promise.
It is a direction vector.

🌌 Age 16 — Wrote my first orbital simulation out of curiosity
🌱 Now — Building reproducible AI-controlled systems and engineering pipelines on GitHub
🛠️ Early 20s — Deepen foundations in engineering, AI, and control
🚀 Long-term — Contribute controllers tested beyond simulation

The exact path is unknown.
The mission continues anyway.