Video yükleniyor...
Video Yüklenemedi
Mondo's Beni ( may look cute, but there is a serious learning stack inside. It can perform flips, navigate autonomously, and track moving subjects. The secret is reinforcement learning: Beni practices at massive scale in simulation, optimizing its controller through millions of virtual trials before entering the real world.... show more
31,539 görüntüleme • 3 ay önce •via X (Twitter)
24 Yorum

1/ @nvidia IsaacLab and @GoogleDeepMind MuJoCo are core parts of our training and validation pipeline. We train controllers across large numbers of simulated environments, then validate them in a separate physics engine.

2/ Modern legged-robot reinforcement learning builds on years of excellent research. The work of @rdn_nikita , David Hoeller, and their collaborators has been particularly influential. For Beni’s flipping motions, we also drew inspiration from @xbpeng4’s classic DeepMimic paper. Beni has been reading the literature—and practicing its backflips.

3/ Some behaviors need an additional layer of sim-to-real polish. For these, we use ideas from Regularized Online Adaptation, or ROA, developed by my good friends @zipengfu and @xuxin_cheng (and @deepakpathak) :

4/ Along side all the neural networks, Beni also uses some good old-fashioned SLAM—in several creative ways. One particularly valuable tool is GTSAM, created by @fdellaert and contributors.

5/ Beni’s target-tracking system also contains several algorithmic innovations developed by our own team. These improvements allow Beni to follow a subject smoothly while coordinating perception, locomotion, and camera motion. We may publish more technical details in the future.

Sometimes the best robotics system is not “AI versus classical robotics.” It is AI standing on top of decades of great robotics research. Small robot. Serious algorithms. 🤖

is there a developer mode on Beni? like, could he be used for people learning to do RL on real robots in a fun way?

Great work @Shuo — love to see our ROA algorithm in action. This robot is so cool!

and you want $549 for this?!

Thats a really cool toy!

咦?你是RM的杨硕吗?

Great to see Beni’s acrobatics @ShuoYangAIR!

We love to see it ! Really good partnership opportunity with @UFBots

that’s cool!

Solid stuff

Now I want Beni to kick back 🤣 Love that lil guy, even though I don't have a use for him!

can we train it ourselves similar to microduck and use a simulator? @pollenrobotics

this is awesome

This is great for cat parents.. Best toy for cats to ay with.. Consider cheaper version.

have you experimented with world models for simulation?

😈 cute! But was the kick necessary?! 😰

a cutie, how hes following the skater 🥰

The simulation scale is the impressive part, and it is also where the hard problem hides. Training at that volume gets you a policy that is optimal for your simulator, including its contact model, its friction constants and its integration step. The gap to hardware is not noise, it is systematic, and domain randomization tends to paper over it rather than measure it. The number I always want to see alongside these is how much the policy degrades on the real robot before any fine tuning. That one figure tells you how good the physics model actually was.

Probably cannot last long on batteries

