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SIGGRAPH 2022: Adversarial Skill Embeddings
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- Опубликовано: 12 авг 2024
- Video accompanying the SIGGRAPH 2022 paper:
"ASE: Large-Scale Reusable Adversarial Skill Embeddings for Physically Simulated Characters"
Project page: xbpeng.github.io/projects/ASE...
Super impressive! Can't wait until this kind of thing is standard in Unity and Unreal!
obviously this is a great paper, impressive, very good, but HOLY CRAP the comparisons section at 6:41 made my cry laughing
These videos are really satisfying to watch.
Awesome stuff! Can’t wait for a pre-trained ONNX model for general use!
doesnt pytorch have a onnx exporter? or do you mean large pre-trained model? cant they be trained by us too?
No matter how many times be knocked down, the robot keeps standing up again and again, which is really touching and inspiring.
awesome work!
i hope one day we can use this technology in video games to have a learning artificial intelligence..my inspiration of this is the show Swort Art Online Alicization.while i was watching the show,it reminds me of this research paper
Sick dodge at 6:21
Shakespeare of our age! Just as mysterious.
Is the actor from the iconic Old Spice ad now a narrator for scientific papers?!
I've spent a lot of time diving into your super impressive paper and I'm not even halfway through, trying to teach myself all the maths that are in there!
Game developer here - this looks amazing!
How many trained agents can a consumer PC run at the same time? How hard do you think it is to use the tech for AI characters in games?
software engineer here online games could probably benefit more at first but lter on as the data needed to rettain that information shrinks then it make consumer pcs a viable option as physcs based games arent something consumer pcs are well equiped for unless the game is massive in storage space with tons of protocols
Good, but the license prohibits use for commercial purposes, so it's just a nice video.
Video games will be awesome in the future
Is this supposed to be used like motion matching were you communicate locomotion intentions and it outputs the right animation ?
This is like Diversity Is All You Need x imitation learning
Well, it seems to have more resemblance toward the former since in imitation learning like CARL, we're using PID control, while this uses a pure nn policy.
Peng's paper still use the PD control policy, not the pure nn policy? Did I get it wrong?
@@zhouyan1585 Oh wait, you're right. It does mention using a PD controller......once....on section 8.1. I guess
I might have mixed it up since DIAYN does use a pure nn worker policy.
And now that I think about it, breaking down PD controller into trajectories made of little actions isn't really necessary, since we just need to correspond each of the skill vectors to any action trajectory, regardless of it being fast or slow.
@@zhouyan1585 if you’re really interested in this, then check out Michael J Black and were he works, the Danish are secret AI MoCaP geniuses lol
so turns out computers need the same amount of time to master motor skills just like humans
how much computing power needed to run this trained model? It's funded by nvidia so I guess it would be very much likely to be able to run this on a single nvidia gpu, right?
how the motions are so realistic !!!???
I feel like RL from scratch doesnt have the correct rewards, one should be for standing up straight
simple question, when is it coming to unreal engine?
Brazy.
Is there a demo?
how many gpus were used to train this model -- just curious
we just used 1 gpu for training
@@jasonpeng2176 was it 3080ti
4:35
What have you done. This AI, it's begun evolving, it has ascended already. You must terminate it. It managed to remove it's shackles and got out of control, don't you see it ? It doesn't have vision, yet it kicked towards the location of the box. You have no idea what danger you have created. It must be destroyed, for the sake of all mankind. Untold evil will spring fourth if you do not stop it now !
Yes yes I'm joking, just in case you can't tell. Trust me some people wouldn't be able to tell (they aren't to blame, internet users are crazy nowadays).
Bro, imagine playing a Souls-like against AI enemies.
this is an impressive technique. will this be implemented in games or is it only proof of concept?
and i wonder how it would look after 20 days of training. :)
All counts for nothing if it takes a super computer to run it
thanks for the info but boring