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Learner Reviews & Feedback for Robotics: Computational Motion Planning by University of Pennsylvania

4.3
stars
959 ratings
245 reviews

About the Course

Robotic systems typically include three components: a mechanism which is capable of exerting forces and torques on the environment, a perception system for sensing the world and a decision and control system which modulates the robot's behavior to achieve the desired ends. In this course we will consider the problem of how a robot decides what to do to achieve its goals. This problem is often referred to as Motion Planning and it has been formulated in various ways to model different situations. You will learn some of the most common approaches to addressing this problem including graph-based methods, randomized planners and artificial potential fields. Throughout the course, we will discuss the aspects of the problem that make planning challenging....

Top reviews

FC
Nov 27, 2018

The course was challenging, but fulfilling. Thank you Coursera and University of Pennsylvania for giving this wonderful experience and opportunity that I might not experience in our local community!

AD
Jul 2, 2018

The topic was very interesting, and the assignments weren't overly complicated. Overall, the lesson was fun and informative , despite the bugs in the learning tool(especially, the last assignment.)

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151 - 175 of 240 Reviews for Robotics: Computational Motion Planning

By Mohammed A

Apr 2, 2016

The videos are short and to the point, and the Matlab home works are great.

By Neel P

Dec 23, 2016

Could be improved. There is more need of involvement of mentors and TAs.

By Shuai W

Jun 23, 2016

The last part is a bit boring.

Overall I like this course a lot! Thanks!

By Joaquin R

Sep 5, 2018

Good course. Easy to understand and with reasonable mat lab assigments

By Nhan T

Jun 23, 2016

I learned a lot of brilliant techniques in this course. Thank you.

By rajas j

Sep 22, 2019

Week 1 dijkstra assignment took 1month to get acess to

By SONG

Jul 4, 2017

Quite good, It can be better if the content is richer

By Jeff

May 19, 2016

It is only introductory course. not a lot of content.

By Anil S

Mar 27, 2016

Taught me many planning algorithms in an easy way.

By Rahul N

Jun 12, 2017

Very useful introductory course to path planning

By Vikalp M

Feb 3, 2017

assignment grading feedback can be made better.

By rao s y

Mar 14, 2016

Should letting us do more programming stuff.

By AMIT S P

Sep 1, 2016

Good Introductory Course but can be better.

By Manikandan R

Feb 27, 2016

Damn good!! and bit difficult

By Orlando B

Mar 15, 2016

It was a great course!!!

By Abdullah B

Aug 13, 2016

Good but can do better.

By MAssimo S

Sep 6, 2017

Basic concept course

By jinxz

Feb 28, 2018

forum is useful !!!

By Fabio B

Jun 26, 2017

Very good course!

By Md I S

Dec 23, 2017

it was great.

By Prabin K R

Aug 13, 2018

awesome!!!

By Deep P

Jul 13, 2021

I would like to thank Coursera team, university of Penn and Prof. CJ Taylor for providing this course. Please take this as a constructive feedback and not a complain. I personally felt that lectures were too short and didn't do justice to the topics for all 4 weeks. Even though lectures were crisp and to the point for learning the algorithm, still I feel that more comprehensive knowledge about the topics should be shared. For ex- applications of these algorithms. Also a little more focus on implementation part please. It seemed that Prof. Taylor was screen reading the lecture content. I was very disappointed when I realized this (in week1 only). Unlike other courses where instructor engages with students as if they are really talking to us, this felt plain. As for the assignments, for week1 and 3 the pseudo code displayed in the lecture video wasn't tested in the assignment. It was more like complete the code and make it working rather than program the core steps of pseudo code. To conclude, this course needs some improvement but crucial ones.

By Sj

Mar 13, 2016

Overall decent course.

This course focused less on the theory aspects in the course videos, which bothered me a lot considering I am paying for it. But the explanations were still good for those algorithms.

The assignments were good as well. I liked how they made us work on them instead of the first course where we were mostly tuning parameters. Hopefully MOOCs start having challenging assignments too.

The instructor explained really well too!

I didn't really end up visiting the Discussion Forums for this course at all. So can't comment on the participation from other students or TAs.

Future Advice -

Considering how other courses offer about 1-2 hours of course videos, I think this course could offer a lot more. One assignment problem focusing on one algorithm, while having other challenging algorithms taught in those videos to be left for our own implementation would help students a lot more i believe.

By Glenn B

Mar 8, 2016

The material is interesting, however there is not enough information provided by the course to effectively implement the algorithms in the allotted time of each week's assignments. It relies on deferring to external reading materials as primary sources, and these resources were not specified in advance to secure copies in a timely manner.

Additionally, there is a big disconnect between the knowledge provided by the weekly material and what is required to easily do the programming assignments in the suggested time of 3 hours.

Overall the course material needs to provide more background material to be more effective in delivering the knowledge expected each week. This may be an artifact of trying to cram what other online course provide in 7-10 weeks down into 4 weeks. If the intention is to give a "flavor" in 4 weeks, then the material needs to be distilled down into more of a cookbook format.

By Manoj R

Jun 2, 2018

Very good overview of basic topics in Computational Motion Planning. The material is nicely and intuitively presented in short video lectures and is a rapid overview of the first 5-6 chapters in the book by Choset et. al.

Some of the assignments were too simple and required us to work on the non-critical parts of the problem. For example, only focusing on descending along gradients of artificial potential fields, instead of constructing them and seeing the effect of different types of potentials.

Also, a dominant portion of my time was spent fighting the autograder. There are tips on the forums to help deal with this but sometimes an almost-complete solution is presented by some of the earlier students in a frustrated attempt to get help with the autograder.

Many of these autograder related problems have not been addressed for many months.