Chevron Left
Вернуться к Robotics: Estimation and Learning

Robotics: Estimation and Learning, University of Pennsylvania

4.2
Оценки: 297
Рецензии: 76

Об этом курсе

How can robots determine their state and properties of the surrounding environment from noisy sensor measurements in time? In this module you will learn how to get robots to incorporate uncertainty into estimating and learning from a dynamic and changing world. Specific topics that will be covered include probabilistic generative models, Bayesian filtering for localization and mapping....

Лучшие рецензии

автор: VG

Feb 16, 2017

The material is clearly presented. The Matlab exercises complement and reinforce the subject, the level of difficulty is well balanced, thanks for this great course.

автор: NN

Jun 20, 2016

This is course is really helpful for beginners to understand how probability is useful in Robotics.Assignments are bit tough but worth the time .

Фильтр по:

Рецензии: 70

автор: Joaquin Rincon

Sep 22, 2018

Lack of detailed content, assigments WAY too difficult if you just take into account what was explained.

автор: Aryan Agarwal

Sep 21, 2018

Great course learnt a lot !!

автор: Vu Nhat Minh

Sep 19, 2018

This is a really comprehensive course which gave me a good knowledge about Gaussian Model and Kalman Filter ...

автор: Yuanxuan Wang

Aug 15, 2018

Good course schedule, but videos in week 2 and week 4 really need some rework. There are errors in slides and videos are too vague to be helpful, I have to look for external materials to understand the topics (Kalman Filter and Particle Filter).

автор: Juan Álvaro Fernández Muñoz

Aug 04, 2018

All in all, it's a very interesting, absolutely necessary topic for robotics. But everything is treated here without theory tests, detailed examples and the like, so learning is only tested with programming tasks. The student must work a lot with MATLAB to come up with crafty solutions for week practices.

автор: juha nieminen

Jul 15, 2018

Assignments need some serious revising.

автор: Saurabh Mirani

Jul 06, 2018

The course structure is nice. However there is little explanation for the programming assignments, especially the last one (week 4). For other weeks I got good help from the forums however the forums do not have much threads and many are unanswered. It would be great if more reading material can be added for that week.

автор: Shounak Das

May 23, 2018

good course ..expecting more follow up courses on this topic !

автор: Matthew Pearson

May 14, 2018

This course covers some very important techniques in modern robotics including Kalman filters, mapping, and Particle filters. However, the way that these topics are presented in this course is not very clear. The later lectures especially lack the necessary content to provide a clear understanding of advanced topics. The final assignment in particular is very poorly documented and the included instructions are a bit misleading. In addition to that, the forums seem to have been abandoned by the course instructors and are full of unanswered questions from struggling students, some of them more than a year old. This course needs some serious attention and revision. Definitely the lowest quality course of this series.

автор: Shaun Laidlow

Apr 12, 2018

The professor left all the teaching to his Phd students. The material was not straight forward, and possibly made even more difficult with the lackluster slides and presentation. A pdf explaining the theories would be more helpful.