College application research evidence · understanding and original contribution · Seoul and online
Project courseThe student keeps the work and the record they made.
UPenn HALO: Finding Drone Targets from Language
Grades 9-12: language-guided drones, maps built from...
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.
- University of PennsylvaniaOpen 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 UPenn HALO: Finding Drone Targets from Language a good fit for high school students in Grades 9-12?
UPenn HALO: Finding Drone Targets from Language is a good fit for students in grades 9-12 who are interested in language-guided drones and maps built from camera views. CIT offers online or in-person lessons in Apgujeong, Gangnam, Seoul, in one-to-one or small-group formats. Students make a drone search app that clarifies vague goals, a map screen of targets and flight paths, and LLM safety test records.
The course is built on an open project published by UPenn. Students give a simulated drone a goal in plain words, such as 'find the chair', and follow the route HALO takes to reach it, starting from a map built from camera views with the kinds of targets labeled on it. By rewording only the instruction they compare which target and path the drone picks, then build a feature that shows candidates and asks again, without moving, when an instruction is vague. An optional large language model (LLM) only turns free-form instructions into allowed target and place candidates and passes on only goals a person has approved. 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 University of Pennsylvania. Names identify the source, not affiliation or endorsement.
- University of PennsylvaniaOpen project it is built on
How can I explain this course to my child?
If questions about language-guided drones or maps built from camera views keep making you ask why, UPenn HALO: Finding Drone Targets from Language lets you investigate the question with evidence, then build and defend an extension of your own.
Which interests suggest this course?
- language-guided drones
- maps built from camera views
- clarifying vague instructions
What does the student finish?A drone search app that clarifies vague goals, a map screen of targets and flight paths, and LLM safety 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 labeling target types on a camera-built map shapes the search path.
- Reword only the instruction and compare how the chosen target and flight path change.
- Set the conditions under which the drone holds still, shows candidates, and asks again, then code that feature.
- Test with normal, vague, and risky requests that the LLM passes on only approved goals.
- University of PennsylvaniaOpen project it is built on
How does the course progress?
- 1Understanding the Research Question and Baseline Behavior
Check the simulated drone and its safe operating range, then build a map of the surroundings from camera views.
- 2Connecting Sensors, Actions, and Measurements
Label target types on the map and trace how a plain-language instruction leads to a target and a flight path.
- 3Building a Feature and Control Screen from Evidence
Reword only the instruction to compare results, then code a feature that shows candidates and asks again when a goal is vague.
- 4Testing the Integrated App and Explaining the Design
Connect a map screen of candidates, paths, and searched areas with the optional LLM step, then test it with normal, vague, and risky requests.
What counts as useful evidence?
A drone search app that clarifies vague goals, a map screen of targets and flight paths, and LLM safety test records.
What should an admissions reader be able to see?
The course is built on an open project published by UPenn. 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
- KumarRobotics/HALO
- pinned commit
eeb361ef02bc - commit date 2026-07-17
- licence No license file found at review; inspect upstream terms before reuse
- text files 151
The student changes one map setting on an indoor search drone and sees what speed costs in coverage. In class the project is separated into five boxes (input, memory, process, output, control). The lesson opens one of them, changes one value, and leaves the rest closed.
- InputWhat comes in?
The forward camera view, and the object a person named in words
- Memoryopened hereWhat persists?
The map that cuts seen space into cells and records each as free or blocked
- ProcessWhat transforms?
Choosing the next place to go from the frontiers not yet seen, and joining a path to it
- OutputWhat leaves, and who uses it?
The path it flew, and the log of where it looked and when
- ControlWhat decides when anything runs?
What decides whether it is now exploring, approaching or stopped, and which nodes are running
InputView and target
air_sem_explorer/air_sem_explorer/node/mapper_node.pyair_sem_explorer/config/mapper_ros_sim.yaml
MemoryThe cell map
air_sem_explorer/air_sem_explorer/mapper/occ_map.pyair_sem_explorer/air_sem_explorer/mapper/frontier_detector.py
ProcessChoosing where next
air_sem_explorer/air_sem_explorer/planner/exploration_planner.pyair_sem_explorer/air_sem_explorer/planner/path_tracker.py
OutputPath and the log
air_sem_explorer/air_sem_explorer/utils/evaluation_logger.pyair_sem_explorer/air_sem_explorer/mapper/frontier_visualizer.py
ControlState and start-up
air_sem_explorer/air_sem_explorer/planner/state_machine.pyair_sem_explorer/launch/planner.launch.py
The files the lesson opens, by name. The course is not a walk through the repository; it opens a chosen few and says which.
One value moves and everything else stays. The right-hand column is the prediction written before the run, not a result; the work is reconciling the two.
On a narrow screen, swipe the picture sideways.
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.
- University of PennsylvaniaOpen project it is built on
Questions parents search before choosing this course
Is UPenn HALO: Finding Drone Targets from Language a good fit for high school students in Grades 9-12?
UPenn HALO: Finding Drone Targets from Language is a good fit for students in grades 9-12 who are interested in language-guided drones and maps built from camera views. CIT offers online or in-person lessons in Apgujeong, Gangnam, Seoul, in one-to-one or small-group formats. Students make a drone search app that clarifies vague goals, a map screen of targets and flight paths, and LLM safety test records.
Can my child take UPenn HALO: Finding Drone Targets from Language 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 UPenn HALO: Finding Drone Targets from Language?
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 UPenn HALO: Finding Drone Targets from Language?
The main evidence is a drone search app that clarifies vague goals, a map screen of targets and flight paths, and LLM safety test records. Students also document decisions, tests, feedback, and limits in age-appropriate language.
How can UPenn HALO: Finding Drone Targets from Language show research understanding in a college application?
The course is built on an open project published by UPenn. 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 UPenn HALO: Finding Drone Targets from Language 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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