Animesh Garg ✓

@animesh_garg · San Francisco / Atlanta · joined 04 Jan 2010

Foundation Models for Generalizable Autonomy in Robotics. Reinforcement Learning. Faculty in AI Robotics @GeorgiaTech. Prev @nvidia @UCberkeley @stanford

36 005Followers

1 425Following

2 898Posts total

242.1KViews on collected posts

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Animesh Garg ✓@animesh_garg1 Sep

Went on @MTSlive to chat about cool new developments in robotics. I am rather excited about the return of the RL, albeit in new packaging. And the microduck is truly the talk of the town. I am always excited for new platforms, and this one is definitely pretty good. We can 5.4K views · 33 likes · 4 reposts · 5 replies Open on X →

Animesh Garg ✓@animesh_garg31 Aug

Georgia Tech prof. @animesh_garg predicts the next 2 years of robotics will be "the return of the RL" as the field shifts from collecting data to learning from it: "Pillar one: how do you represent the environment? Should the input be objects? Should the input be completely http 17.7K views · 48 likes · 5 reposts · 2 replies Open on X →

Animesh Garg ✓@animesh_garg31 Aug

@animesh_garg That is because H-1B flocks into tech. We have 5+ million Indians in this country from 30 years of H-1B -> Green Card conversions... 90% of them work in tech. That is a huge percentage of Tech jobs in the US. We aren't letting you steal our Tech industry anymor 681 views · 68 likes · 2 reposts · 3 replies Open on X →

Animesh Garg ✓@animesh_garg31 Aug

here is another viewpoint H1b is a sliver of a sliver! (rounding error at best) https://t.co/bJWukr4UyS 10.1K views · 23 likes · 3 reposts · 15 replies Open on X →

Animesh Garg ✓@animesh_garg31 Aug

Fighting over H-1Bs while ignoring the core macro shifts in our economy is the definition of penny-wise and pound-foolish. 99.6% of the US workforce has nothing to do with H-1Bs, yet 80% of tech and labor discourse acts like it’s the single defining issue of our economy. The 146.3K views · 804 likes · 66 reposts · 275 replies Open on X →

Animesh Garg ✓@animesh_garg31 Aug

Academic research in US is a value creation engine that creates at least $3 in societal return for every $1 of public investment. The 2027 cycle will be real test of US graduate programs value proposition. 1. Top students will reevaluate their options, and may not tolerate the 30.8K views · 225 likes · 12 reposts · 9 replies Open on X →

Animesh Garg ✓@animesh_garg29 Aug

The ambition of adapting a policy from a single online demo remains the goal. The modern generalist foundation models achieve "emergent" physical in-context learning from video prompts. It is also a reminder that task specification can often be a code generation task as long as 3.9K views · 28 likes · 5 reposts · 4 replies Open on X →

Animesh Garg ✓@animesh_garg25 Jun

Physical AI is making a classic Moneyball error by mis-pricing data. Optimizing for "cumulative operational hours" or relying on low-variance production telemetry is the robotics equivalent of chasing batting averages, when we should be isolating on-base percentage. Unlike http 27.2K views · 233 likes · 21 reposts · 13 replies Open on X →

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7 posts des 90 derniers jours, à côté de la tranche de 10K–100K abonnés. diffusé largement, mais peu de ces spectateurs réagissent.

Vues médianes10 147ce compte924médiane pour 10K–100K

Portée, %28.18%ce compte3.62%médiane pour 10K–100K

Engagement, %0.80%ce compte1.52%médiane pour 10K–100K

Indicateur	Ce compte	Médiane pour 10K–100K	Rapport
Vues médianes par post	10 147	924	11.0×
Portée (vues ÷ abonnés)	28.18%	3.62%	7.79×
Taux d'engagement	0.80%	1.52%	0.52×

Autres comptes de cette tranche → Comparer avec un autre compte → Comment ces repères sont établis →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

27.2K25 Jun

3.9K29 Aug

30.8K31 Aug

146.3K

10.1K

681

17.7K

5.4K1 Sep

Last 8 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

1.00%25 Jun

0.94%29 Aug

0.81%31 Aug

0.80%

0.40%

10.72%

0.32%

0.77%1 Sep

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.

What the audience does

Likes61.5%1 462 in total

Reposts5.0%118 in total

Replies13.7%326 in total

Quotes1.2%28 in total

Bookmarks18.6%442 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.

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