Deepak Pathak ✓

@deepakpathak · Pittsburgh, PA · joined 27 May 2013

Co-Founder & CEO @SkildAI, Faculty @CarnegieMellon. PhD @UCBerkeley; BTech @IITKanpur I study topics in AI (robotics, machine learning & computer vision).

30 789Followers

422Following

962Posts total

5.8MViews on collected posts

Derniers posts

Deepak Pathak ✓@deepakpathak10 Sep

Scaling S1 training with @nvidia GPUs, then deploying S1 in the factory to build even more GPUs! 11.3K views · 127 likes · 13 reposts · 9 replies Open on X →

Deepak Pathak ✓@deepakpathak10 Sep

@SkildAI's new S1 robot foundation model helps robots learn previously unseen tasks from a single video demonstration. 🤖 See how NVIDIA technology supports S1 from training and simulation to real-world deployment. Learn more ➡️ https://t.co/k3qyGBSvke https://t.co/MjEuMykiPi

0:16

31.6K views · 234 likes · 30 reposts · 9 replies Open on X →

Deepak Pathak ✓@deepakpathak10 Sep

We just hit 100M ARR, 10 months after our first deployment. One of the fastest growing physical companies in human history. Deeply grateful to our team and partners for making this achievement possible. We're just getting started. 51.8K views · 432 likes · 32 reposts · 29 replies Open on X →

Deepak Pathak ✓@deepakpathak10 Sep

We just hit 100M ARR within 10 months of starting deployments. We are in factory lines. On construction sites. In kitchens. In data centers. Cleaning. Welding. Building. Cooking. Deploying. https://t.co/7Hqjc3SXul

1:52

1.7M views · 2.3K likes · 308 reposts · 111 replies Open on X →

Deepak Pathak ✓@deepakpathak9 Sep

If S1 makes a mistake, it tries again. For example, the input video prompt here attached the wheel just once. During deployment the wheel didn't align properly, so S1 re-aligned it. By watching the prompt, S1 understands the goal and improvises to get there: https://t.co/Q0mV6R

0:47

239.5K views · 694 likes · 103 reposts · 36 replies Open on X →

Deepak Pathak ✓@deepakpathak3 Sep

Big day for open source. Congratulations to @huggingface and @nvidia! 3.6K views · 31 likes · 0 reposts · 1 replies Open on X →

Deepak Pathak ✓@deepakpathak29 Aug

One surprising aspect of S1’s in-context learning (ICL) is where it shines most: super long-horizon tasks (10+ min) and scenarios outside the pretraining distribution. For short, simple pick-and-place tasks (5–20 sec), most frontier models can already perform well either https:/ 16.4K views · 157 likes · 30 reposts · 5 replies Open on X →

Deepak Pathak ✓@deepakpathak28 Aug

In-Context Learning Results Hint at a “GPT Moment” for Robotics For general-purpose robots to be useful and economical, you need to be able to teach them new skills on the fly. And now we can see how this will work, with in-context long horizon video demonstrations shown by http 26.3K views · 127 likes · 18 reposts · 10 replies Open on X →

Deepak Pathak ✓@deepakpathak27 Aug

Feels nice when folks actually read a long blog carefully. Makes countless hours spent with the team obsessing over every single word totally worth it! This is something I learned the hard way over the years from my dear advisor -- Alyosha Efros. Those who know him, know. Haha. 14.5K views · 138 likes · 6 reposts · 3 replies Open on X →

Deepak Pathak ✓@deepakpathak26 Aug

We’re scaling S1 as fast as we can. The curve shows no signs of slowing down, steadily going up. We’ve been working for a while to find this kind of scaling law in robotics. 11.1K views · 134 likes · 26 reposts · 6 replies Open on X →

Deepak Pathak ✓@deepakpathak26 Aug

This is a good question. If the task was only 5–10s long, it can be difficult to distinguish true physical steerability from generalization. But we’re doing tasks that run for over 10 minutes and not seen during training (OOD). Sustaining performance at such long horizon and 12.7K views · 80 likes · 4 reposts · 3 replies Open on X →

Deepak Pathak ✓@deepakpathak25 Aug

@deepakpathak Nice work, Deepak. Congrats! Now you should do it with multi-finger hands, not these silly parallel jaw grippers :-) 18.4K views · 69 likes · 1 reposts · 1 replies Open on X →

Deepak Pathak ✓@deepakpathak25 Aug

@SkildAI what happens if the robot accidentally drops something, does it panic or retry? 67.3K views · 5 likes · 0 reposts · 0 replies Open on X →

Deepak Pathak ✓@deepakpathak25 Aug

Very exciting! But I still have the same question for @SkildAI as I did for Generalist's GEN-1.5: does S1 exhibit *physical prompt steerability*, where different prompts induce distinct behavior in the *same* environment? I like Figs. 5 & 6 because they start to get at this 16.6K views · 38 likes · 1 reposts · 1 replies Open on X →

Deepak Pathak ✓@deepakpathak25 Aug

@deepakpathak This is such an inspirational release. I should be able to teach my robot everything from my own small examples. 710 views · 8 likes · 0 reposts · 1 replies Open on X →

Deepak Pathak ✓@deepakpathak25 Aug

Scaling laws for in-context learning for robotics, perhaps the most exciting result in robotics https://t.co/jJYxAf9AzX 37.5K views · 397 likes · 35 reposts · 8 replies Open on X →

Deepak Pathak ✓@deepakpathak25 Aug

@deepakpathak These are very promising results - now let’s hillclimb! 565 views · 10 likes · 0 reposts · 1 replies Open on X →

Deepak Pathak ✓@deepakpathak25 Aug

In-context learning for robotics is here. - Long-horizon tasks over 10 minutes long - Never seen during pre-training - Prompted with one video, no fine-tuning We are building intelligence from the foundations up, not from the top down. 72.7K views · 515 likes · 54 reposts · 33 replies Open on X →

Deepak Pathak ✓@deepakpathak25 Aug

Introducing S1, our new foundation model that learns from one example. It can be taught 10-minute long tasks that it has never seen before, from one video prompt without any fine-tuning. Watch S1 operate in real-time via in-context learning: https://t.co/wmF3Byv179

5:14

3.5M views · 7.1K likes · 1K reposts · 444 replies Open on X →

Face aux comptes de taille comparable

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

Vues médianes18 400ce compte924médiane pour 10K–100K

Portée, %59.76%ce compte3.62%médiane pour 10K–100K

Engagement, %0.89%ce compte1.52%médiane pour 10K–100K

Indicateur	Ce compte	Médiane pour 10K–100K	Rapport
Vues médianes par post	18 400	924	19.9×
Portée (vues ÷ abonnés)	59.76%	3.62%	16.5×
Taux d'engagement	0.89%	1.52%	0.58×

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

16.6K25 Aug

67.3K

18.4K

12.7K26 Aug

11.1K

14.5K27 Aug

26.3K28 Aug

16.4K29 Aug

3.6K3 Sep

239.5K9 Sep

1.7M10 Sep

51.8K

31.6K

11.3K

Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.25%25 Aug

0.01%

0.39%

0.68%26 Aug

1.52%

1.01%27 Aug

0.62%28 Aug

1.18%29 Aug

0.90%3 Sep

0.35%9 Sep

0.17%10 Sep

0.98%

0.89%

1.31%

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

What the audience does

Likes59.3%12 530 in total

Reposts8.0%1 681 in total

Replies3.4%711 in total

Quotes3.7%772 in total

Bookmarks25.7%5 419 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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