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Class Information

These sections contain information about how the class is run

Class and Office Hours Schedule

Professor: Pablo Frank Bolton (pfrank at smith)

Research interests: Human-Robot Interaction, Robotic Perception, STEM education

Class Schedule: Thursday, 1:20 PM – 4:00 PM at Bass 204

Attendance is mandatory.

Contact for Class stuff: Use Slack (fastest), office hours (most detailed), or can also email me (slowest).

Office Hours:

  • TBD : (we’ll set these up if we find we need them)

Objectives and Structure

Objectives

Robotic perception is a crucial aspect of robotics. It lets artificial agents, like robots or software automatons, gather information about their environment to make informed decisions. Perception includes sensing of light information (vision), sound information (audition), tactile or limb-position information (touch / proprioception) and several other aspects that can be integrated to give the agent a sufficient idea of its context. In this seminar, we will discuss the basics concepts, the history and future directions of robotic perception. The objective of this course is:

  • To develop an understanding of “perception” as data acquisition for decision making.
  • To learn to decompose, analyze, and synthesize academic papers focused on robotics.
  • Explain core algorithms involved in robot perception and navigation
  • Discuss how/where robotic perception makes makes an impact on our society and where it is going.

Effort Expectations: This class has the expectation that you have mathematical maturity and programming experience, but most importantly: that you can devote the time to the necessary work and study to do well in class. The course is a 4-credit course, which means you should expect to work around 12 hours per week.

The recommended distribution is:

  • approximately 3 hours of class time
  • approximately 9 hours every week on homework assignments
    • reading
    • writing
    • library research
    • poster design
    • other activities

If you follow this general routine, the class should not be too challenging. If you do not follow this time investment, you will feel accumulating pressure.

Summary: Please put the time in to make sure you don’t feel unnecessary stress.

Content:

We’ll discuss the following themes:

  • Introduction to Robotics
  • Mobile Robotics Theory
  • Perception in Robotics
  • Computer Vision for Robotics
  • Mapping in Robotics
  • Navigation in Robotics
  • Simultaneous Localization and Mapping (SLAM)

In addition, we will discuss the incidence of robotics in society, develop strategies and solutions for problem scenarios, and demonstrate some of the concepts and issues with live demonstrations using the Misty robotics platform.

Class mechanics:

  • We will discuss concepts using blackboard annotations as well as projected presentations and animations;
  • Readings will be assigned and should be completed before each designated lecture. This will help prepare you for the day’s discussion.
  • In addition to the discussion sessions, we will have a “lab” time where we will play with the Misty; We will discuss the particulars of Misty’s operation and extrapolate to other contexts, robots, and uses.

Assignments:

You will have reading assignments with an accompanying deliverable that will be discussed in class. If you do not do the readings or bring your required deliverables, discussion will be hampered. The readings will usually be academic papers or book chapters, but we could extend them to include documentaries, interviews, YouTube videos, etc.

For every assignment, you will be asked to analyze the information and synthesize it into either a short essay, a poster-like presentation, or something similar, depending on the objectives.

In addition to these, you will be asked to make at least 3 presentations throughout the semester: two assigned subjects and the final presentation (on a topic of your choice).

Prerequisites and Student Responsibilities

Prerequisites:

  • CSC250 and/or Instructor permission. The former may be taken concurrently with permission of the instructor. Most importantly, I will assume you have basic “mathematical maturity”: i.e., that you are comfortable both reading and writing academic-type papers.

Responsibilities:

  • Attendance: You should attend all classes unless you have a valid excuse.
  • Interact, ask questions, and generally participate in class discussions.
  • Complete the assigned preliminary readings and activities before each lecture.
  • Complete problems individually unless working in a group as specified on the assignment in which case you can work only with those group members. We do plagiarism detection so don’t throw the course away.
  • When working with a group, it is essential that each group member pull their own weight, but also that other group members let them do so!

Course Philosophies

Throughout the class, students should focus on adhering to the following general tenets:

  • Try it! – A common question is “will this work?”, or “what will happen in this case?”. The only reasonable answer is “try it and see!”.
  • It is OK to make mistakes! – an error is one learned lesson. After trying something, having that fail is as much a datapoint as a correct path. Note that we usually want correct paths after exploring and making a lot of errors so keep looking!
  • Ask for help when stuck! – if you have 1) tried multiple paths and 2) you have explored the problem and made many mistakes, and you are still stuck, please seek assistance! that’s what were here for! Believe it or not, we actually like teaching and helping you make progress.
  • Know your sources, and use them!
  • Be proud of your submissions! – Clarity above all. Use proper styling, simplify it where you can to make it more understandable, and comment it where appropriate.
  • Planning is the best problem-solving tool! – You should not jump into writing essays or designing posters before thinking about it thoroughly. Design your replies/comments by breaking the reading materials into logical parts that make sense independently and when put together make a logical argument.
  • Practice methodical analysis! – Spend time “stepping though” the algorithms you read about, statement by statement to understand the logic behind them, and why it is logically sound (or why it is only an approximation). Do not submit a commentary that you don’t fully understand with the hope of flying under the radar. We look deeply at your submissions to gauge your level of understanding.

Course Materials

  • Webpage for the course: Course Webpage
  • Schedule for the course: Course-Schedule
  • Moodle: Course full name “CSC353pm-01: Seminar: Topics in Robotics-Robotics Perception and Mapping”
  • Slack: You'll receive an invitation to the workspace: csc-353pm-01-202401.slack.com

Books:

There is no required textbook for the course. We will assign readings from academic journals and publicly available robotics textbooks. That said, many readings will come from the following freely available books:

  • Nikolaus Correll, Bradley Hayes, Christoffer Heckman and Alessandro Roncone. Introduction to Autonomous Robots: Mechanisms, Sensors, Actuators, and Algorithms, MIT Press, 2022. Git Repo for the Book
  • Notebook-based book “Introduction to Robotics and Perception” by Frank Dellaert and Seth Hutchinson. Online Book

Grading

There are three aspects that will be considered for grading:

  • Participation in discussions and activities (based on readings) … 40%
  • Deliverables (write-up / diagrams / code / etc) … 30%
  • Presentations (based on readings and work on deliverables) … 30%

In this course, we will employ a variation on mastery-based grading (thanks to professor Alicia Grubb). The objective is not to get things perfectly the first time but to improve on subsequent attempts in the path to the mastery of a subject or a skill. It is my job to help scaffold your writing and thinking as well.

In this course, all work will be graded as “completed/satisfactory” (S) or “yet-to-be-completed” (N: Not yet). Sometimes our first attempt at an assignment falls flat, and there are many reasons that this may occur. In this class, a first attempt that is not strong enough yet (either through writing, analysis or idea development) will be given a N (for not yet). Any N received must be revised until it meets all requirements and receives an S. All revisions to N must be completed by the last day of classes unless a previous arrangement has been requested (with me or the registrar’s office). The key to improving is refining and scaffolding our ideas. We will work together to achieve this.

For presentations, the grading will be slightly different: you will not be required to do a perfect job or know all the answers, you will only need to try, learn from any feedback, and do better in the next presentation.

I will also strive to give you a small peak into the proper development of academic papers and the process of evaluating the work of others. To this effect, we will sometimes work in groups to collaborate on a deliverable, or to critique each other’s work.

Assignment Rubrics:

  • Marks. Your submission will receive one of two marks:
    • S: indicates that the submission meets the expectations of the assignment, including complex and careful thinking, attempts to stretch own ideas and explore own thoughts, and explain positions through data in the literature.
    • N indicates that the submission is not there yet (up to standards). After feedback is given on N, the assignment will need to be revised and submitted until it receives an S. Assignments that receive an N mark MUST be re-submitted within one week of return or two weeks after initial submission (whichever is later), unless otherwise negotiated with the instructor. When it is re-submitted, you will include a short section called “Replies to Reviewer” that explains specifically how you addressed the comments and limitations from the prior submission (bullet points are acceptable).

Although most of your work will have many strengths and many things to work on, I will only highlight a few so that you can know what to improve the next time around. I think it is better not to be overwhelmed by feedback, but for you to have feedback that you can actually respond to. I may also make comments in the margin, but those will be mostly for ideas or things I want you to consider. I may do corrections if I think it will model a point that I would like to make.

Grade Calculation:

(The following grade calculation may be modified slightly depending on how the semester progresses)

  • All mandatory written assignments outlined in the syllabus must be completed to assignment specifications to receive a grade in the course. Assignments graded as (S)will receive an A.

  • Failure to complete any one of these requirements satisfactorily will result in either (at the discretion of the instructor): (1) the termination of the mastery grading contract and traditional grading for the remainder of the course; or (2) assignment of a letter grade C (75%) for each outstanding N, E (0%) for work never submitted, and traditional grading for the Final Presentation.

  • Final Presentation will be based on a final term paper. We will work toward a complete term paper and slide presentation throughout the course in 3 different review stages. The presentations will be 15 minutes long and should reflect the work submitted in the term paper. The subject of these will be chosen by you and should have a small “research” component to them (experimental / survey from a literature review / deep analysis of an academic paper/ etc. ); While this project will constitute a large portion of the homework you will do toward the end of the semester, it is still graded as a normal presentation.

Accommodations:

As individuals, we learn in different ways. I try to vary the activities used during the course to suit a variety of learning patterns, and I am always open for suggestions. Please come talk to me if you have an idea that will make the course more accessible to you and/or other students. If you need special accommodation, like extended exam time, please submit requests for accommodations in writing with proof of College support from the Office of Disabilities Service within the first two weeks of class. Let me know if you need help with this process.

Academic Honesty

Team assignments require collaboration amidst each team, but no collaboration between teams is permitted. If you did not work in a team then you are not allowed to collaborate on the homework assignments. We use software to compare submissions, so please don’t risk it. If you’re having significant trouble with an assignment, please contact me.

Please check the Student Handbook to see the rules for Academic Integrity.

Just as you can do a google search for code online, it is trivial for us to do the same. If you feel pressured about an assignment, please come see me instead of cheating.

Use of Generative AI: Tools like ChatGPT are now widely available and very useful for some tasks. In this course, the ultimate purpose is the mastery of concepts and skills which will be ultimately evaluated through live discussions and presentations; this is not something these tools will help with. If you wish to use these tools for bootstrapping or as inspiration, you must cite their use and indicate the how you used and modified their contribution in a section called: “Use of Generative AI”.

Academic and Mental Health Resources

The following are resources available to you that may provide assistance and support during the semester. They provide help for learning, mental health, and wellness.

Learning resources:

Mental Health and Wellness resources:

Additional support resources:

Anonymous Feedback Form

We will add a link inside Moodle to an anonymous feedback form so you can let us know if there is anything getting in the way of your learning.

Acknowledgement

This policy was borrowed with permission from the policy designed by Alicia Grubb, in the CS Department, Smith College, and that was originally a derivative work of a policy created by Shannon Audley, Associate Professor, Education & Child Study, Smith College.

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