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Harvard AirLearning: Teaching Drones to Avoid Obstacles
Grades 9-12: drone obstacle avoidance, reinforcement...
Where this course comes fromThe universities and labs named here made the open projects or materials this course draws on. CIT designed the course independently; it is not an official, affiliated, or endorsed course of those institutions.
- Harvard UniversityOpen project it is built on
- Free consultation and level check
- Apgujeong classroom or online
- One-to-one or small group
- Registered academy no. 11882
Is Harvard AirLearning: Teaching Drones to Avoid Obstacles a good fit for high school students in Grades 9-12?
Harvard AirLearning: Teaching Drones to Avoid Obstacles is a good fit for students in grades 9-12 who are interested in drone obstacle avoidance and reinforcement learning. CIT offers online or in-person lessons in Apgujeong, Gangnam, Seoul, in one-to-one or small-group formats. Students make a drone training simulation app with custom obstacle scenes, a flight test screen, and safety-stop code with test records.
The course is built on an open project published by Harvard. Without a real drone, students use the AirLearning simulator to test how well a drone avoids collisions as obstacles multiply or camera data becomes inaccurate. They first check that the older software (AirSim and Unreal) still runs, then study how the drone learns through repeated practice (reinforcement learning) and the points it earns for good actions. Next they change only the obstacle count or the camera error, compare results, and build a safety stop that halts the drone when collision risk rises, separate from what it has learned. The student then designs and tests an extension of their own: a new question, feature, model, interface, or solution. In an application or interview, the student separates the source research from their own decisions, results, failed attempts, revisions, and limits.
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My child found the AI classes interesting and stayed with them. It made for a worthwhile school break. Thank you for teaching so attentively.
- Age group
- Grades 9-12
- Academic subject
- AI & Data, Engineering & Robotics, Physics & SpaceBrowse subject
- Course type
- Course
- Format
- Online or Apgujeong in person · one-to-one or small group
- Teaching language
- Korean by default, with complete English materials
- Curriculum status
- Lesson-readyEvery learning item includes the evidence, worked example, practical work, and tutor guidance required by CIT's lesson-ready standard.
From open research to student-owned work
Students inspect a relevant public source, reproduce the idea, then add an original question, feature, or test. The material this course reads was made at Harvard University. Names identify the source, not affiliation or endorsement.
- Harvard UniversityOpen project it is built on
How can I explain this course to my child?
If questions about drone obstacle avoidance or reinforcement learning keep making you ask why, Harvard AirLearning: Teaching Drones to Avoid Obstacles lets you investigate the question with evidence, then build and defend an extension of your own.
Which interests suggest this course?
- drone obstacle avoidance
- reinforcement learning
- flight safety with noisy cameras
What does the student finish?A drone training simulation app with custom obstacle scenes, a flight test screen, and safety-stop code with test records.
Is this a good fit?
A strong fit for students who want to understand, build, test, or responsibly use AI systems.
Course placement follows current subject and coding readiness.
Students should be ready to document sources, methods, and limits.
Advanced tools are introduced after a clear baseline.
What will my child learn?
- Explain how learning by practice and the points given for good actions shape a drone's flight.
- Change only the obstacle count or the camera error and compare how collision results change.
- Check whether older simulation software still runs, and record what failed and what could not be verified.
- Design and test a safety stop that halts the drone when collision risk rises, whatever the learned behavior does.
- Harvard UniversityOpen project it is built on
How does the course progress?
- 1Understanding the Research Question and Baseline Behavior
Check that the older software runs on today's computer, then map what the simulated drone senses around it and how it moves.
- 2Connecting Sensors, Actions, and Measurements
Learn how each action is scored within one practice run, then run the basic obstacle scene and log the results.
- 3Building a Feature and Control Screen from Evidence
Change only the obstacle count or camera error, compare results, and code a safety stop for rising collision risk.
- 4Testing the Integrated App and Explaining the Design
Connect a test screen for obstacle layout, flight path, collision rate, and learning score, then demo the finished app in three minutes.
What counts as useful evidence?
A drone training simulation app with custom obstacle scenes, a flight test screen, and safety-stop code with test records.
What should an admissions reader be able to see?
The course is built on an open project published by Harvard. The student works out what those researchers were trying to learn, why the question matters, and how they tested it, then designs and tests an extension of their own: a new question, feature, model, interface, or solution. In an application or interview, the student separates the source research from their own decisions, results, failed attempts, revisions, and limits.
Useful evidence may include a documented dataset, baseline comparison, model evaluation, error analysis, and a clear record of the student's own decisions.
These names identify who published the open project the course starts from. The evidence an application reads is what the student understood, completed, and can explain.
The real research project this course reads
- harvard-edge/AirLearning
- pinned commit
9ec5bab2bbec - commit date 2021-09-13
- licence No license file found at review; inspect upstream terms before reuse
- text files 2
This course works from a pinned version of harvard-edge/AirLearning. Which files it opens, and what it changes, is still being written; it goes here when it is ready.
The universities and labs named here made the open projects this course reads. CIT designed the course independently; it is not an official, affiliated, or endorsed course.
- Harvard UniversityOpen project it is built on
Questions parents search before choosing this course
Is Harvard AirLearning: Teaching Drones to Avoid Obstacles a good fit for high school students in Grades 9-12?
Harvard AirLearning: Teaching Drones to Avoid Obstacles is a good fit for students in grades 9-12 who are interested in drone obstacle avoidance and reinforcement learning. CIT offers online or in-person lessons in Apgujeong, Gangnam, Seoul, in one-to-one or small-group formats. Students make a drone training simulation app with custom obstacle scenes, a flight test screen, and safety-stop code with test records.
Can my child take Harvard AirLearning: Teaching Drones to Avoid Obstacles online or in person, one-to-one or in a small group?
Yes. CIT offers online and in-person lessons at its Apgujeong academy in Gangnam, Seoul, with one-to-one and small-group options. A readiness consultation confirms the available format and starting point for the course.
Does my child need prior subject knowledge or coding experience for Harvard AirLearning: Teaching Drones to Avoid Obstacles?
Course placement follows current subject and coding readiness. Students should be ready to document sources, methods, and limits. Advanced tools are introduced after a clear baseline.
What will my child make or practice in Harvard AirLearning: Teaching Drones to Avoid Obstacles?
The main evidence is a drone training simulation app with custom obstacle scenes, a flight test screen, and safety-stop code with test records. Students also document decisions, tests, feedback, and limits in age-appropriate language.
How can Harvard AirLearning: Teaching Drones to Avoid Obstacles show research understanding in a college application?
The course is built on an open project published by Harvard. The student works out what those researchers were trying to learn, why the question matters, and how they tested it, then designs and tests an extension of their own: a new question, feature, model, interface, or solution. In an application or interview, the student separates the source research from their own decisions, results, failed attempts, revisions, and limits. Useful evidence may include a documented dataset, baseline comparison, model evaluation, error analysis, and a clear record of the student's own decisions.
How are the schedule and tuition for Harvard AirLearning: Teaching Drones to Avoid Obstacles determined?
CIT confirms the student's readiness, goal, location, class size, and current availability before recommending a course plan. The consultation and level check are free; tuition is explained before enrollment.
When should a student start?
There is no fixed intake month. CIT reviews the student's current school term, readiness, and available hours, then names the point in the course where they should begin.
I appreciated that a simple early idea was extended into an 'AI debate tool'. My child has always been interested in debate and social issues, so seeing that interest connect to the project makes me expect a more complete and distinctive result.
Where else parents check CIT
Choose the course after a readiness check
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