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Project courseThe student keeps the work and the record they made.
Stanford robomimic: Learning Robot Actions from Demonstrations
Grades 9-12: learning from human demonstrations...
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.
- Stanford · UT AustinOpen 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 Stanford robomimic: Learning Robot Actions from Demonstrations a good fit for high school students in Grades 9-12?
Stanford robomimic: Learning Robot Actions from Demonstrations is a good fit for students in grades 9-12 who are interested in learning from human demonstrations and demonstration quality. CIT offers online or in-person lessons in Apgujeong, Gangnam, Seoul, in one-to-one or small-group formats. Students make a robot demonstration manager app, demonstration-count comparison charts, and demo-filtering code with test records.
The course is built on an open project published by Stanford · UT Austin. Using the robomimic example, students follow how a robot learns motions from human demonstrations. They see how one motion is stored from start to finish, replay a demonstration, and compare runs that use numeric data with runs that use camera images. After checking how success rates change when only the number of demonstrations changes, they build a feature that labels demonstrations as successful, shaky, or stopped and a management app that tracks the chosen demonstrations through to training results. 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 Stanford · UT Austin. Names identify the source, not affiliation or endorsement.
- Stanford · UT AustinOpen project it is built on
How can I explain this course to my child?
If questions about learning from human demonstrations or demonstration quality keep making you ask why, Stanford robomimic: Learning Robot Actions from Demonstrations lets you investigate the question with evidence, then build and defend an extension of your own.
Which interests suggest this course?
- learning from human demonstrations
- demonstration quality
- numeric data versus camera images
What does the student finish?A robot demonstration manager app, demonstration-count comparison charts, and demo-filtering 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?
- Check what information a demonstration stores and replay a saved demonstration.
- Measure how the number of demonstrations affects the robot's success rate.
- Build a feature that labels demonstrations as successful, shaky, or stopped and bundles only the chosen ones.
- Connect an LLM that suggests quality tags so that only tags the student confirms are used.
- Stanford · UT AustinOpen project it is built on
How does the course progress?
- 1Understanding the Research Question and Baseline Behavior
Run a prepared robot demonstration and see how one motion is recorded from start to finish.
- 2Connecting Sensors, Actions, and Measurements
Replay human demonstrations and compare numeric-data and camera-image methods under the same conditions.
- 3Building a Feature and Control Screen from Evidence
Compare success rates by number of demonstrations and build a feature that bundles only the chosen demonstrations.
- 4Testing the Integrated App and Explaining the Design
Finish the app with a screen linking video clips, success rates, and typical failures, then demonstrate it.
What counts as useful evidence?
A robot demonstration manager app, demonstration-count comparison charts, and demo-filtering code with test records.
What should an admissions reader be able to see?
The course is built on an open project published by Stanford · UT Austin. 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
- ARISE-Initiative/robomimic
- pinned commit
d309eaecc18a - commit date 2026-08-09
- licence MIT
- text files 163
The student compares directly how the amount and quality of demonstration data change what a robot learns. 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 demonstration file and the training configuration
- Memoryopened hereWhat persists?
The dataset of demonstrations, the rules for which observations count, and the weights that training changes
- ProcessWhat transforms?
The training step that nudges weights toward predicting the action from the observation
- OutputWhat leaves, and who uses it?
The saved policy file, and the success rate from running it in the environment
- ControlWhat decides when anything runs?
The loop that decides how many epochs to run, when to evaluate and when to save
InputDemos and config
robomimic/scripts/train.pyrobomimic/config/base_config.py
MemoryDataset and weights
robomimic/utils/obs_utils.pyrobomimic/algo/algo.py
ProcessOne training step
robomimic/algo/bc.pyrobomimic/utils/train_utils.py
OutputThe saved policy
robomimic/utils/train_utils.pyrobomimic/envs/env_base.py
ControlThe training loop
robomimic/scripts/train.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.
- Stanford · UT AustinOpen project it is built on
Questions parents search before choosing this course
Is Stanford robomimic: Learning Robot Actions from Demonstrations a good fit for high school students in Grades 9-12?
Stanford robomimic: Learning Robot Actions from Demonstrations is a good fit for students in grades 9-12 who are interested in learning from human demonstrations and demonstration quality. CIT offers online or in-person lessons in Apgujeong, Gangnam, Seoul, in one-to-one or small-group formats. Students make a robot demonstration manager app, demonstration-count comparison charts, and demo-filtering code with test records.
Can my child take Stanford robomimic: Learning Robot Actions from Demonstrations 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 Stanford robomimic: Learning Robot Actions from Demonstrations?
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 Stanford robomimic: Learning Robot Actions from Demonstrations?
The main evidence is a robot demonstration manager app, demonstration-count comparison charts, and demo-filtering code with test records. Students also document decisions, tests, feedback, and limits in age-appropriate language.
How can Stanford robomimic: Learning Robot Actions from Demonstrations show research understanding in a college application?
The course is built on an open project published by Stanford · UT Austin. 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 Stanford robomimic: Learning Robot Actions from Demonstrations 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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