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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

Shuo Yang

5,468 subscribers

31,539 Aufrufe • vor 3 Monaten •via X (Twitter)

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24 Kommentare

Profilbild von Shuo Yang

Shuo Yangvor 3 Monaten

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.

Profilbild von Shuo Yang

Shuo Yangvor 3 Monaten

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.

Profilbild von Shuo Yang

Shuo Yangvor 3 Monaten

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) :

Profilbild von Shuo Yang

Shuo Yangvor 3 Monaten

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.

Profilbild von Shuo Yang

Shuo Yangvor 3 Monaten

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.

Profilbild von Shuo Yang

Shuo Yangvor 3 Monaten

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. 🤖

Profilbild von Carlos DP 🤖🇺🇸

Carlos DP 🤖🇺🇸vor 3 Monaten

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?

Profilbild von Deepak Pathak

Deepak Pathakvor 3 Monaten

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

Profilbild von JVM

JVMvor 3 Monaten

and you want $549 for this?!

Profilbild von cr_ss_

cr_ss_vor 2 Monaten

Thats a really cool toy!

Profilbild von HGONG | Founder & Researcher

HGONG | Founder & Researchervor 3 Monaten

咦？你是RM的杨硕吗？

Profilbild von Ankur Deka

Ankur Dekavor 3 Monaten

Great to see Beni’s acrobatics @ShuoYangAIR!

Profilbild von Keith Botha

Keith Bothavor 3 Monaten

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

Profilbild von Jrmginer

Jrmginervor 3 Monaten

that’s cool！

Profilbild von Swiftlined

Swiftlinedvor 3 Monaten

Solid stuff

Profilbild von tobitege

tobitegevor 3 Monaten

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

Profilbild von Will Kriski

Will Kriskivor 2 Monaten

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

Profilbild von theobot

theobotvor 3 Monaten

this is awesome

Profilbild von Sadikk

Sadikkvor 2 Monaten

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

Profilbild von Tanay Jaipuria

Tanay Jaipuriavor 3 Monaten

have you experimented with world models for simulation?

Profilbild von Black Eyed Hat Man

Black Eyed Hat Manvor 3 Monaten

😈 cute! But was the kick necessary?! 😰

Profilbild von T.

T.vor 3 Monaten

a cutie, how hes following the skater 🥰

Profilbild von AI Apps API

AI Apps APIvor 3 Monaten

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.

Profilbild von Hoon_Sky

Hoon_Skyvor 3 Monaten

Probably cannot last long on batteries

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