ME 449 Robotic Manipulation

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Revision as of 11:25, 26 September 2020

Fall Quarter 2020

In Fall 2020, ME 449 will be taught as a hybrid course. Initially, at least, in-person meeting times will be treated as office hours, with a combination of worked examples by the instructors, questions from the class (online or in person), and time to work on assignments with an instructor available if you get stuck. All lectures are pre-recorded for viewing any time, and experiments with robots will be in simulation. You do not have to attend in-person meetings for this course, nor do you have to attend online during the course meeting periods, though that would allow you to interact with me and others, live. Advantages of being in person may be a better spatial awareness of your classmates and the instructor, better access to typical social cues that are hard to get through a computer screen, and, as a result, better engagement with others (while respecting social distancing), but there will be no specific material or experiences available only to those who attend in person. As the quarter progresses, we will evaluate the pros and cons of in-person class sessions.

Important Information

  • All class sessions will be recorded and available in Canvas afterward. (The two sessions were combined into one in Canvas on Sept 16.)
  • The first two class meetings (Sept 16 and 18) will be online only. We will not meet in LR2 until Monday Sept 21. After that, we will evaluate whether an in-person component adds significant value.
  • For ME grad students: the ME 512 conflict has been resolved by eliminating the time for 512. You should be able to register for ME 512 now.
  • We will use this wiki, the Canvas page, and Coursera extensively in this course. Since many of the materials of this course are taken from the Modern Robotics specialization on Coursera, you will see a lot of references to "courses 1-6," "the specialization," "week 1," etc. You can ignore those. We are not following the same schedule as used on Coursera, we will not use all the materials on Coursera, and we will have items that are not a part of Coursera. Ignore any automatic emails from Coursera! I can't control those.
  • Although it may be tempting, because most of the materials are already available on Coursera, please do not work more than a week ahead of the schedule posted below. For one reason, we may change the content during the course, so your early work may be wasted. For another, you will be out of sync with the content of the class sessions. (On the other hand, make sure you don't fall behind!)
  • Zoom meetings will be recorded and placed on Canvas. No one other than class members will be able to access the recordings. Given these circumstances, I encourage you to engage as much as possible.
  • Zoom teaching is new to many of us, but students actually see more examples of it than faculty! So if you have suggestions how to improve the course, they are more than welcome.
  • I encourage you to order your (free) doc cam, that points your laptop's webcam downward and facilitates sharing real-time written work with me. Make sure you are authenticated to NU's network, then order at You can learn more at

Getting Started

Do the following things as soon as possible:

Supportive Class Environment

All members of this class (instructors, TAs, students) are expected to contribute to a respectful, inclusive, and supportive environment for every other member of the class.

We are partners in your education; help me help each of you get the most out of this class. Please engage as much as possible during our class meetings! (e.g., via discussion and chat)

Honor Code

By far the most important purpose of this course is to prepare you for further study, or employment, in the field of robotics! But of course it is also our duty to provide a fair evaluation of your performance. You are encouraged to discuss the material with the instructor, course assistants, and your classmates, but you are not allowed to share your answers or code with others. Anyone asking for answers or code, or providing answers or code, or becoming aware of others doing so without reporting to the instructor, is considered in violation of the honor code.

Class Info


Course Summary

Representations of the configuration and spatial motion of rigid bodies and robots based on modern screw theory. Forward, inverse, and differential kinematics. Robot dynamics, trajectory planning, and motion control. Wheeled mobile robots and mobile manipulation.


Linear algebra, first-order linear ODEs, freshman-level physics/mechanics, a bit of programming background.


50% of your final grade will be from your Coursera grades (which I expect to be near perfect) and 50% from projects outside of Coursera, including the student-generated exercise and the capstone project.

Course Text and Software

This course uses the textbook Modern Robotics: Mechanics, Planning, and Control, Kevin M. Lynch and Frank C. Park, Cambridge University Press 2017. If you find an error or typo in the book, please report it here.

Get the book, install and test the Modern Robotics code library, and install and test the CoppeliaSim robot simulator. You will program in Python, Mathematica, or MATLAB in this course.

Here is a linear algebra refresher appendix to accompany the book.

Approximate Syllabus and Schedule

Here is a summary of the structure of the course:

  • Before most classes, you will watch the associated videos on Coursera and answer the "lecture comprehension" quizzes. (Designed to be relatively quick, to solidify your understanding.)
  • You are encouraged to read the corresponding portions of the textbook after watching the videos. I suggest you watch first, then read, then possibly re-watch, but you can determine what works best for your learning style.
  • During the class period after those videos, I will typically summarize what we learned, work a problem, take any questions you have about the material, and possibly assign you a problem to work on.
  • We have two kinds of quizzes on Coursera: "lecture comprehension" quizzes, which are short and immediately follow lectures, and summative quizzes, which are usually longer assessments/assignments at the middle or end of a chapter. All quizzes are required and have an impact on your grade. You are requested to stick (at least approximately) to the schedule posted below, but there will be no penalty if a quiz is submitted late.
  • Within Coursera we also have "discussion prompts," open-ended group questions that you should reply to (responses can be simple) and forums where you can post questions and reply to other students' questions.
  • We also have a few assignments, including the student-created exercise and the capstone programming project, that will be submitted through Canvas instead of Coursera. More information is forthcoming.

Below is the approximate syllabus and schedule. Next to each date is the Coursera material that should have been covered before that class.

Chapter 2, Configuration Space

  • Fri Sept 18: through Chapter 2.2 (dof of a robot)
  • Mon Sept 21: through Chapter 2.3 (c-space topology and representation)
  • Wed Sept 23: finish Chapter 2 (task space and workspace)

Chapter 3, Rigid-Body Motions

  • Fri Sept 25: through Chapter 3.2.1 (rotation matrices SO(3))
  • Mon Sept 28: finish Chapter 3.2 (angular velocities, so(3), exponential coordinates)
  • Wed Sept 30: through Chapter 3.3.2 (transform matrices SE(3) and twists)
  • Fri Oct 2: finish Chapter 3 (se(3), exponential coordinates, wrenches)

Chapter 4, Forward Kinematics (skip section 4.2 on URDF)

  • Mon Oct 5: finish Chapter 4 (product of exponentials formula, space and e-e frame)

Chapter 5, Velocity Kinematics and Statics

  • Wed Oct 7: through Chapter 5.1 (space Jacobian, body Jacobian)
  • Fri Oct 9: through Chapter 5.2 (statics of open chains)
  • Mon Oct 12: through Chapter 5.4 (singularity analysis, manipulability)

Chapter 6, Inverse Kinematics (focus on section 6.2)

  • Wed Oct 14: Chapter 6 (numerical inverse kinematics)
  • Fri Oct 16: catch up

Chapter 8, Dynamics of Open Chains (skip sections 8.4, 8.7, 8.8, and 8.9)

  • Mon Oct 19: through Chapter 8.1.2 (Lagrangian dynamics)
  • Wed Oct 21: Chapter 8.1.3 (understanding the mass matrix)
  • Fri Oct 23: Chapter 8.2 (dynamics of a single rigid body)
  • Mon Oct 26: Chapter 8.3 and 8.5 (Newton-Euler inverse dynamics, forward dynamics)

Chapter 9, Trajectory Generation

  • Wed Oct 28: through Chapter 9.3 (point-to-point trajectories, polynomial via point trajectories)
  • Fri Oct 30: Chapter 9.4 (time-optimal time scaling)
  • Mon Nov 2: catch up, final project
  • Wed Nov 4:

Chapter 11, Robot Control (focus on sections 11.1 through 11.4)

  • Fri Nov 6: up to (not including) Chapter (linear error dynamics)
  • Mon Nov 9: finish Chapter 11.2.2 (first- and second-order error dynamics)
  • Wed Nov 11: through Chapter 11.3 (motion control with velocity inputs)
  • Fri Nov 13: Chapter 11.4 (motion control with torque or force inputs)

Chapter 13, Wheeled Mobile Robots (skip section 13.3)

  • Mon Nov 16: through Chapter 13.2 (omnidirectional wheeled mobile robots)
  • Wed Nov 18: Chapter 13.4 (odometry)
  • Fri Nov 20: Chapter 13.5 (mobile manipulation)
  • Mon Nov 23:
  • Wed Nov 25:
  • Mon Nov 30: wrap-up
  • Mon Dec 7: Capstone project due

Video Lectures and the Flipped Classroom

This course will take advantage of video lectures. You will watch the videos on Coursera, but you also can see the video lectures at the video browser or using direct links to the videos on YouTube.

The general flow of the class will be the following:

  • Before class, watch the videos, take the lecture comprehension quizzes associated with each video, and do the associated reading. In general, I recommend that you first watch the videos to get a quick understanding of the material of the chapter, then follow up by reading the appropriate sections of the book. The videos are short and dense, so don't expect to get by only watching the videos. You will need to read the book, then do the exercises, to gain mastery of the material.
  • In class, I will usually briefly review lecture comprehension quizzes, work an example problem, take questions, and be available as you work on assignments.

Practice Exercises

Sample exercises and their solutions, useful for practicing your understanding of the material.

Practice Quizzes

Student-Created Exercises

All students will be responsible for creating a practice exercise, consisting of the exercise and the solution. A good exercise should test an important concept in the context of a real robotics application (e.g., motion planning for a quadrotor, robot localization, computer vision, grasping, etc.), require the learner to understand and apply equations in the book or use the book's software, and require a bit of thought (i.e., not just "plug and chug" questions). For many exercises, a good figure or two is helpful. You could use a figure of a real robot and add your own annotations to it (e.g., frames or objects in its environment), or you could hand-draw something, or you could use CoppeliaSim or other software to help create the figure. You should not confine your question to an application discussed in the textbook. Make your exercise interesting and motivating! Exercises that require synthesizing two or more concepts or equations are more interesting and useful. Think about what kind of exercise would have helped you to really understand the material. Your questions should be very clearly worded, so anyone can understand it without you having to be there to interpret it for them.

You should look at the practice exercise document and end-of-chapter exercises for inspiration, but obviously your exercises should not be copies.

You will create your exercise using LaTeX (pronounced "lay teck" or "lah teck"), the standard for scientific document preparation. Overleaf is a free online implementation of LaTeX. To get started on your exercise,

  1. Download this .zip file and uncompress it. There are five files: main.tex, prelims.tex, twist-wrench.pdf, table-lamp.PNG, and LampSolution.PNG.
  2. Create an account on Overleaf.
  3. Create a new (blank) project on Overleaf called "exercise."
  4. Upload the five files to this project. (You may get a warning that your default main.tex file is being overwritten; don't worry about it.)
  5. Click on main.tex to see your main LaTeX document.
  6. Press the "Recompile" button to see the pdf document that is compiled from the five files. You can download the pdf file, or all the "source" files, by clicking on "Menu" and choosing which to download. This is the .pdf file you should have created.

main.tex is the main file of the project, and the only one that you will edit, so you should understand what is going on in that file. prelims.tex tells LaTeX what packages to use and defines some macros, e.g., \twist creates \mathcal{V} and \wrench creates \mathcal{F}. The other three files are image files that get included in the document. You will create different image files depending on your exercise. For example, you can make a nice hand drawing and then scan it.

To learn more about typesetting in LaTeX, google is your friend! Try googling "latex math" or "latex math symbols," for example.

You will turn in the source for your exercise as a zip file, as well as the final pdf file.

Final Project: Mobile Manipulation

The final project, described on this page, is due on Canvas on Monday December 7. Reminders:

  1. Read and follow closely the instructions on what to submit! If you are missing requested files, or if you use a different directory structure, you will lose points. Make sure your top-level README file is clear on what you've done and what you've submitted.
  2. If your code does not work well, please describe the remaining issues in your README file. Don't gloss over them or only provide examples where the code works well if the code does not work well for other example problems. Otherwise, if the graders find problems with your software, you will not receive credit for having identified them yourself.
  3. You can get up to 10 pts of extra credit for correctly implementing joint-limit avoidance (so the robot links and chassis do not self-intersect) and singularity avoidance (e.g., using joint limits that keep the arm in a portion of its workspace where it does not encounter any singularities). If you implement these, it is best to submit examples of your code solving the same problem two ways---not using joint-limit avoidance and using it---so the usefulness of the joint-limit avoidance is apparent.
  4. Make sure to keep your problem inputs separate from the code. The exact same code should solve all your problem instances; you shouldn't have different copies of your code for different problem inputs. You could have an input file for each of your examples (e.g., bestScript, overshootScript, newTaskScript) which defines the inputs (e.g., block configurations, controller gains, initial robot configuration) and invokes your code. Then a grader just needs to invoke those scripts to verify your results. (If you implemented joint-limit avoidance, this could just be one of your inputs, e.g., a variable called "avoidJointLimits" which is 0 if you don't care about avoiding joint limits and 1 if you do.)
  5. Make sure your videos are good quality. They shouldn't be too fast (at least 5 seconds long) or low resolution. The motion should be smooth.
  6. If your code is written in Python, indicate which version of Python should be used.
  7. If you submit your code as part of the MR library, make it easy for the grader to find your code (e.g., collect it all in one place and indicate in the code or your README where to find it).

ME 449 Assignment 1

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