Відео

Does anyone else shed a tear from watching this? These videos used to be on like 5x the speed. Incredible progress.

I wonder how well this compares against liquid neural network which can also model system dynamics

I'd like to see this but with fast weights for realtime adaptation implemented in "standard" robots

2:18 the front limbs "picking up" the rear ones strongly resemble trapeze, very mesmerizing

Bhaiya i m from india hatsoff yaar i am happy for u , i am preparing for bank exams in india and while preparing today i read about mobile aloha , and i immediately searched for it and i was surprised even an indian person is part of this project feeling very proud.❤❤❤❤kabhi future mai ek podcast de dena abhi toh mai tyari kr leta hun 😅

Will you upload timelapse videos of the training? With the simulated robot doing random movements for an hour until it learns to sort of walk and stuff?

haha that one of the most free style D455 mounting i ever see

It's all fun and games until they obtain self-awareness

aye arj yo xi ye ye

0:55 , this is so cool

holy shit

😻 Promo-SM

Gonna be so weird when our robot dogs are better at talking than we are.

that is impressive. I have a go1 Edu. let me know if I can contribute somehow

Impressive real-world results. Good job, guys.

Nothing "extreme" about this but okay.

magnifique

folks this it the beginning of the end. it was fun while it lasted😢

How much for a regular consumer ?

Extreme parkour robot: does stair handstands Background music: Wi wi wi wink hu uh wink wi wi wink (yeh)

perfect dog for people with allergies who'd love to join the local park dog agility group

.. mount a taser on it and suddenly it's not so cute anymore xD

A weapon to surpass Metal Gear!

I'm pretty sure I've played the game set 20 years from now when these things are chasing me with ill intent.

Amazing accomplishment!

😂 does it do the handstand in the grass because it's easier to walk in it like that?

I feel like if ALL commercially avalaible quadruped robots had the same program as the one of the video , people would understand what we meant with "legs allow agile movement" , *this* is way more animal-like

incredible, truly fascinating!! Will your lectures in robotics will be uploaded for someone to watch them ( i am electrical eng in analog IC, but in my spare time I was playing with image recogntion and the jetson nano, I would love to buy a robot dog like mini pupper and apply to him some of these)

Awesome Deepak :)

These little scramblers are so cute, I love them 😂💙 the shot of you holding the poor thing by the lead as they scrabble at the ground makes me giggle

this is awesome, time to fireup the 3d printer

Wow! You guys must have your hands full coming out with all these cutting edge methods and designs!

thanks for sharing!!

You guys did a great job. Could you tell us in detail how you created these movements with the help of Ros?

One day they will destroy us

Tá chegando a hora...

Is it me or is that thing kinda cute? Very cool.

The terminators can now open doors. We're all dead.

Very nice work. Are you guys totally focusing on RL based control policy w/o knowing the dynamic model?

impressive, the kinematics are extremely natural-looking, like it has the zoomies. Puppy, puppy, puppy!

Wow, each video is more impressive than the last! And they look so cute, they're trying so hard 😂💙

That leg wiggle at 0:57 is so cute 😂

You're giving it a knife in the first 30 seconds? 😑😂

Interesting work, thanks for sharing.

I like most of this but it feels like it could be even more open-ended. Perhaps this could be further improved by randomizing the target dataset? Instead of taking a fixed dataset as target, take a random subset from a massive combined one. Obviously, by default, this will be very noisy. However, perhaps the way images are drawn from the dataset could *also* be learned: If two images tend to do well on the same search queries and poorly on the same search queries, make them more likely to be drawn for a dataset together, and if they tend to oppose each other, make them less likely to be drawn together. Basically, draw entire distributions of images, which you can then refine over time. A simpler method might be to just rely on CLIP, take a random direction in CLIP's embedding space, and simply sample based on *that.* - Then it tries to learn how to map CLIP vectors to search queries.

When will the African Grey Parrot's "Alex Study Moment" in Intelligent Robotics come? Do you think the Model/Rival method used as a training method for African Grey Parrot's Alex can be used for intelligent robots?

Awesome research! Thrilled to see real world applications of curiosity driven learning.

So is the measurement of the change in the environment just a simple pixel-level binary classifier going "this pixel has changed (more than mere noise would have)"? Very cool work! Almost cute to watch! (I bet the music choice is part of that though lol)