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MIT Robot Arms: See, Grasp, and Plan

Grades 9-12: how robot arms grasp objects, reading...

Where this course comes from

The 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.

Where this course's material comes from
  • MITOpen project it is built on
Open protected courseCheck course fit
  • Free consultation and level check
  • Apgujeong classroom or online
  • One-to-one or small group
  • Registered academy no. 11882
Students working at a lab bench with a robot arm, a wheeled robot and a drone, a robot-arm simulation on the monitor beside them
Quick answer

Is MIT Robot Arms: See, Grasp, and Plan a good fit for high school students in Grades 9-12?

MIT Robot Arms: See, Grasp, and Plan is a good fit for students in grades 9-12 who are interested in how robot arms grasp objects and reading objects with a camera. CIT offers online or in-person lessons in Apgujeong, Gangnam, Seoul, in one-to-one or small-group formats. Students make a robot arm mission design app, a step-by-step mission replay screen, and path-selection code with tests.

The course is built on an open project published by MIT. Can a simulated robot arm find an object with its camera, pick it up, and carry it around obstacles? Students work through the see, grasp, and move steps in order: they read object shape and distance from the camera, choose a grasp point, calculate joint angles that reach the target, and build a feature that picks whichever of two routes is collision-free. An optional large language model (LLM) only turns a short mission sentence into a draft with allowed objects and destinations, and nothing runs until the student approves it. 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.
Parent of a CIT studentTranslated from Korean. One family's experience; the same result is not guaranteed.Read more parent feedback
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 MIT. Names identify the source, not affiliation or endorsement.

Where this course's material comes from
  • MITOpen project it is built on

How can I explain this course to my child?

If questions about how robot arms grasp objects or reading objects with a camera keep making you ask why, MIT Robot Arms: See, Grasp, and Plan lets you investigate the question with evidence, then build and defend an extension of your own.

Which interests suggest this course?

  • how robot arms grasp objects
  • reading objects with a camera
  • collision-free path choices

What does the student finish?A robot arm mission design app, a step-by-step mission replay screen, and path-selection code with tests.

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?

  1. Represent where the robot and objects are and choose a grasp point with stated reasons.
  2. Explain how object shape and distance read from the camera feed into the grasp.
  3. Calculate joint angles that reach a target and build a feature that picks whichever of two routes is collision-free.
  4. Test an LLM feature that turns mission sentences only into allowed object and destination pairs, with an approval step.
Where this course's material comes from
  • MITOpen project it is built on

How does the course progress?

  1. 1
    Understanding the Research Question and Baseline Behavior

    Run a simulated robot arm from the open examples and learn how the positions of the robot and objects are represented.

  2. 2
    Connecting Sensors, Actions, and Measurements

    Read object shape and distance from the camera, choose two places to grasp, and compare the results.

  3. 3
    Building a Feature and Control Screen from Evidence

    Calculate joint angles that reach a target, then build a feature that picks a collision-free route once obstacles and goals are set.

  4. 4
    Testing the Integrated App and Explaining the Design

    Connect a mission screen that replays the see, grasp, and move steps with the optional LLM, then test the full app.

Students documenting and explaining hands-on work at CIT

What counts as useful evidence?

A robot arm mission design app, a step-by-step mission replay screen, and path-selection code with tests.

What should an admissions reader be able to see?

The course is built on an open project published by MIT. 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.

Systems Lens

The real research project this course reads

The student works through a pick example from MIT open course material and explains which computation decides the outcome. 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 station setup file and the point data a camera produces

  • MemoryWhat persists?

    The estimated pose of the object being handled, and the list of waypoints for where the hand should go

  • Processopened hereWhat transforms?

    Fitting point data to an object model to find its pose, and building the hand trajectory that links the waypoints

  • OutputWhat leaves, and who uses it?

    The target poses sent to the robot, and the scene drawn in the browser

  • ControlWhat decides when anything runs?

    The part that wires blocks and advances time. This project does not build that framework; it takes it from Drake.

The 8 files this lesson opens, named and grouped by box

  • InputStation, point data

    • manipulation/station.py
    • manipulation/scenarios.py
    • manipulation/mustard_depth_camera_example.py

  • MemoryPose and waypoints

    • manipulation/icp.py
    • manipulation/pick.py

  • ProcessPose, then path

    • manipulation/icp.py
    • manipulation/pick.py
    • manipulation/clutter.py

  • OutputTarget poses

    • manipulation/station.py
    • manipulation/meshcat_utils.py

  • ControlBorrowed from Drake

    • manipulation/systems.py
    • manipulation/station.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.

The figure showing the pose offset moving, and what gets watched

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.

Open the pinned repositoryOpen the flow diagramWhat the Systems Lens is

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.

Which sources were reviewed?

Catalog reviewed: 2026-10-03

Where this course's material comes from
  • MITOpen project it is built on

Questions parents search before choosing this course

Is MIT Robot Arms: See, Grasp, and Plan a good fit for high school students in Grades 9-12?

MIT Robot Arms: See, Grasp, and Plan is a good fit for students in grades 9-12 who are interested in how robot arms grasp objects and reading objects with a camera. CIT offers online or in-person lessons in Apgujeong, Gangnam, Seoul, in one-to-one or small-group formats. Students make a robot arm mission design app, a step-by-step mission replay screen, and path-selection code with tests.

Can my child take MIT Robot Arms: See, Grasp, and Plan 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 MIT Robot Arms: See, Grasp, and Plan?

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 MIT Robot Arms: See, Grasp, and Plan?

The main evidence is a robot arm mission design app, a step-by-step mission replay screen, and path-selection code with tests. Students also document decisions, tests, feedback, and limits in age-appropriate language.

How can MIT Robot Arms: See, Grasp, and Plan show research understanding in a college application?

The course is built on an open project published by MIT. 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 MIT Robot Arms: See, Grasp, and Plan 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.
Parent of a CIT studentTranslated from Korean. One family's experience; the same result is not guaranteed.Read more parent feedback

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