Thank you very much. Is it possible to get these PPT Slides?

Cheers for these lectures Dr. Prorok they are extremely helpful 🙏

Great video!!

Is the original video removed?

Hi good job. Can you give me your code? I would like to learn from it Thanks a lot

my gf asking me if this viedo is trying to entertain people.

thanks. but I think in 1:00:00 delta hat is from odomotery measurements

thank you!

Coolest shit ever

Fantastic! What's the simulation platform being used ?

what degree is this course included on?

I am having trouble getting your code to run. Is there any way you can help?

great lecture!

Auf jeden Fall ein Banger! Thanks for uploading

13:00 treating odometry data as controls 21:51 bel(x) with bar is the belief before incorporating the most recent measurement data Zt. After including Zt it becomes bel(x)

good job I would like to ask you about the code are used in this project, Can I have the code of this project, I am doing same project but adding artificial potential field with reinforcement learning and I facing some problem in write code.

Nice work, can you share more details of code or any git repo

Thanks, been looking for this. I wonder how well would this apply to Poker, Bridge, Mascarade, and other deduction games. Or maybe 'positive' interaction modern euro board games like Keyflower

Best course ever

Very cool work you've done. Do you have a git repo we can look at?

Watching from Lecture 1... One of the best in the world. Well organized, and very helpful.

Quality lecture!

Can you please provide the assignments and tutorial files?

thank you. excellent video and very well explained... getting me through my finals rn

Do have research paper or tutorials for do experiment about this technology

Thank you for the lecture videos. Can you please provide the assignments and tutorial files?

does any part of these lectures teach ros?

It was an awesome tutorial. you did great. I am waiting for some practical projects. Hope you will designate this request

Thank you!!! This is so interesting!

Great contribution!

Inspiring lectures indeed! Wonder if the slides will be released? Just for personal educational purposes.

Thanks for making this it's really great to see wish more academics would do this opening up education is really great.

The music is fire ahahah

Absolutely amazing video!

Pure Genius!

Can I get the slide used in this lecture? Thanks

Thank you, Prorok, for this expository lecture.

Dear professor, thanks for this great lecture. I would like to ask about the mean-field approach. Is it really a task assignment solution? Because in the example you have shown, the required abilities for each task have already been assigned. And the robots are assigned to the tasks for ensuring the previously assigned abilities. This seems like the relation between global and local path planning for a single robot. Like a global assignment and a local assignment.

Hi, thanks you Your video was very helpful I would appreciate it if you help me understand the concept of using metahueristics algorithm of solving path planning.

Can I get the presentation document?

I've downloaded the entire series of these lectures. Outstanding work, Dr.

Anyone has the assignments?

can i get a soft copy of the lecture

A robot is an autonomous system which exists in the physical world, can sense its environment, and act on it to achieve some goals. (This definition need to be optimized because not all robots fully autonomous, is not it ?)

thanks for these lectures

before even starting the course, I would love to ask please, what should I know in general? Is it enough to know undergraduate math level? what else? It seems there is a reinforcement learning topic? should I know it very well? thank you

Would love to see a presentation of your new work: "Message-Aware Graph Attention Networks for Large-Scale Multi-Robot Path Planning"

You are the best thank you from syria

Thank you for these excellent lectures. Wonderfully explained for an introduction into these topics.

Whoooooooooal