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Opiniones y comentarios de aprendices correspondientes a Robotics: Perception por parte de Universidad de Pensilvania

4.4
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596 calificaciones
164 reseña

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How can robots perceive the world and their own movements so that they accomplish navigation and manipulation tasks? In this module, we will study how images and videos acquired by cameras mounted on robots are transformed into representations like features and optical flow. Such 2D representations allow us then to extract 3D information about where the camera is and in which direction the robot moves. You will come to understand how grasping objects is facilitated by the computation of 3D posing of objects and navigation can be accomplished by visual odometry and landmark-based localization....

Principales reseñas

DA
31 de ene. de 2021

This course was truly amazing. It was challenging and I learned a lot of cool stuff. It would have been better if more animations were included in explaining complex concepts and equations.

SK
31 de mar. de 2018

Outstanding Course! I could always count on Prof.Jianbo to crunch some of the most complex and confusing parts of the course into a much easier understandable language.

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126 - 150 de 162 revisiones para Robotics: Perception

por Luming

22 de sep. de 2020

a little difficult for me,but learn a lot!

por Hussain M A

1 de oct. de 2019

Hard course but lots of good insight.

por Martin X

23 de oct. de 2016

The courses are good and helpful.

por Ali M H

16 de oct. de 2018

Thank you Professors !

por Jeffrey

18 de feb. de 2017

Unclear explaination

por Fredo C

3 de feb. de 2019

Great Course!

por Daniel S

20 de may. de 2017

This course could use some help. It's a very interesting and important topic and is also difficult, but it could be explained better and the tie in between the lecture videos, quizzes and homework assignments could also be better. Some of the quiz questions are not answerable from reviewing the lecture notes and require outside knowledge of linear algebra and rotation mathematics. The assignments should also be better defined and set up so that there is incremental feedback available for the intermediate steps. For example, the last week's assignment has 5 steps, each of which requires a Matlab function to be written. In many online courses, there are "correct" intermediate results given so that each step can be verified before proceeding to the next step. In this assignment, there is not much feedback until you get to the third or fourth step and even then it's not the best. I had an error in one of the functions, but the problem feedback (photo comparisons) showed it as being OK until I submitted it for grading. It's important, since there's no instructor feedback , to provide some means of checking if you're doing things correctly.Some of the terminology used would be more clear if it was standardized; sometimes coordinates are x and y, sometimes u and v, there's also u1, u2, u3 and things like X = [x,y,z,w] and x = [u,v,w]. Its often quite difficult to know what's being referred to it's called x. I did learn a lot from this course, but it could have been a lot easier.

por Rishabh B

10 de jun. de 2016

The course is a very good overall description of the Perception field. The part I really liked is that there was no haste or a concept just superficially discussed - lectures are long and detailed. The presentation of lectures especially from Prof. Jianbo Shi are excellent - to represent Matrices in colours and give a intuitive sense of every formula(especially the Jacobians and treating the image blending process as painting) .

The bad part of this course is that pronunciations of faculties could be a little unclear and hence a very good transcript is required - which in this course is not upto the mark. There were few mistakes on the slides and should be rectified atleast in the pdf of the slides. What this means is that we have to go through some frustration while watching the video first time which gradually improves on second or third view. Also, there is absolutely no participation of teaching staff. A good content should be supplemented with assistance to further enhance learning experience. Few doubts because of this remains unclear and I wish I could have got this sorted in this class.

por Carlos R

14 de may. de 2016

I dont like how this course was presented. The professors are good but the way how they present the course is extremely inefficient. I mean, because the instructor only speaks moving hands from one side to other, it was very difficult to visualize what and where the instructor was referencing to. Eg. a figure with 3 formulas and many variables there was no way to know in what alpha variable in formulas the instructor was talking about, once all formulas had the alpha variable. Also, when trying to describe a 3D environment only moving hands, its quite impossible to determine what and where the instructor is. One suggestion to try to minimize this problem would be try to use a lase pointer or a stick or a pen or something similar to help the student to now where the instructor exactly is. One example of good presentation is the course of ML from Andrew Ng where he writes all the things while speaking which facilitates the student to follow the sequence. Hope this can help.

por Timothy M

9 de jul. de 2017

some interesting material. The Slides for week 2 and 4 are terrible, too condensed with very little explanation on difficult topics. The Homeworks are pretty interesting, the assignments for week 3 and 4 complement eachother very well. the week 2 Kalman filter assignment didn't seem to work. I submitted something in frustration and was very surprised that it was accepted.

por Vladimir K

15 de jun. de 2016

I really loved the dense collection of relevant information, this course is a great introduction to computer vision-related algorithms.

Unfortunately the lecture videos are poorly edited and subtitles are inaccurate, however the slides are quite good and verbose enough to understand every topic.

Assignments are quite good, however formula derivation explanations could be better.

por Tiandong W

2 de jul. de 2019

It's a nice introduction, but a lot of details are not well explained. A lot of typos in homework, epecially in Equations. This is very bad for UX. And few of mentors are maintaining this course. If you ask questions in forum, you can not get repsonse quickly. I suggest that mentors should spend some time to correct typos and upload some supplement materials.

por Pranav K

15 de ago. de 2020

The content in the course and the expanse of knowledge covered is excellent. I would suggest that course be more organized in terms of terminology and usage of symbols. It does take time, but using different notations while explaining the same concepts causes confusion, at least during the learning phase. Overall a great course.

por Ray.Gong

3 de may. de 2018

The content is undoubtedly valuable and instructive, but for some topics in week 3 & 4 the content isn't organized in a good order. Most importantly, no one answers my questions in the forum, and I felt helpless when the lecturer and slides fail to clearly explain some complex concepts.

por Martin Z

6 de mar. de 2019

There were a lot of error in the materials even after all those years. Also the instructor's English is hard to understand sometimes. In addition to that they do a lot of waving around with their hands instead of marking things directly in the pictures.

por Fabio B

4 de ago. de 2017

The course is excellent is computer vision! The only problem it is not didactic at all, so if you don't are familiar with this content it will be very hard (even impossible) to follow.

por Casey B

29 de ago. de 2018

Lectures sometimes scattered and hard to follow. More advanced visuals would be helpful for such a visual subject vs watching lecturers wave hands and point at things.

por Adarsh S

29 de abr. de 2020

Topics are good and comprehensive, but videos are long and difficult to follow, with a lot of additional research necessary to truly understand the concepts.

por Francisco C M M

6 de may. de 2020

The content was great and not so easy, you need good math-linear algebra background, bur for some reason I didn't enjoy the course :/

por Andrey S

13 de abr. de 2017

Too much theory and projects didn't related with real life problems.

por Christos P

22 de dic. de 2020

Visual Perception (Computer Vision) < Perception

Too math oriented

por Wahyu G

15 de ago. de 2018

Very though and little help in the discussion forum

por Alejandro A V

3 de may. de 2016

The videos are so long in time and not very clear.

por Yiğit U

29 de abr. de 2016

Lessons and videos are very long for one week.

por DEEPAK K P

10 de abr. de 2019

Not at all formulated for bachelors