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).
29 928Followers
419Following
868Posts total
223.9KViews on collected posts
Against accounts of the same size
8 posts from the last 90 days, next to the 10K–100K follower range. shown widely, but few of those viewers react.
| Metric | This account | Median for 10K–100K | Ratio |
|---|---|---|---|
| Median views per post | 16 472 | 2 743 | 6.01× |
| Reach (views ÷ followers) | 55.04% | 8.91% | 6.18× |
| Engagement rate | 0.94% | 1.64% | 0.57× |
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Latest posts
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:/
16.3K views · 155 likes
· 30 reposts · 5 replies
29 Aug 2026
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.
14.4K views · 138 likes
· 6 reposts · 3 replies
27 Aug 2026
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
26 Aug 2026
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
26 Aug 2026
What a crazy statement. Makes me wonder whether Dyna did much data filtering for their model to reach 1M egocentric hours. https://t.co/nq6RTeKXoI
42.9K views · 101 likes
· 4 reposts · 6 replies
25 Aug 2026
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
25 Aug 2026
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
25 Aug 2026
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.
72.2K views · 514 likes
· 54 reposts · 33 replies
25 Aug 2026
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