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Charles Qi ✓

@charles_rqi · San Francisco, CA · joined 17 May 2020

Research @OpenAI. Prev: Tesla | Waymo | FAIR | Stanford

11 278Followers
345Following
211Posts total
1.4MViews on collected posts

Latest posts

Great to see a principled study of video model pretraining for robot manipulation. I especially like the evaluation setup: a real-world industrial task and a strong, consistent post-training pipeline to compare pretrained checkpoints. 4K views · 39 likes · 0 reposts · 3 replies Open on X →
Does scaling pre-training on general web video improve a complex manipulation task in real deployment? We scale model size and pre-training compute, and test on one industrial task. Yes. The better a pre-trained model predicts web video, the better its post-trained policy. 🧵 ht
Seven pre-trained video-model checkpoints on one chart. Task performance on a real robot (at-speed completion rate: every requirement met within 100 s, 0 to 100%) rises with pre-training quality (DINO FD on held-out web video, log scale, axis reversed so better is to the right). XS, S, M, L are model sizes. The four M points are one model at 0.08×, 0.18×, 0.37× and 1× of its pre-training compute.
88.7K views · 290 likes · 38 reposts · 33 replies Open on X →
Using LLMs to control robots is not a new idea, but it has drawn more attention recently with Fable and Astra. Computer-use agents can already accomplish many tasks through code rather than mouse and keyboard actions. For robots, the question is how far coding alone can go. My 11.4K views · 96 likes · 4 reposts · 7 replies Open on X →
Recently, there have been a lot of impressive demos of AI agents, like Claude, controlling robots. I wrote a short blog post with my thoughts on the advent of these "robot-use agents." https://t.co/5bUjZlZxCi I think it's an important change in the trajectory of robotics! 196.6K views · 743 likes · 111 reposts · 44 replies Open on X →
Few-shot, in-context learning for robotics is here. Super exciting! Unlike GPT-3, whose pretraining data largely came from the Internet, GEN-1.5 was trained on large-scale, deliberately collected physical interaction data. A strong signal that scaling real-world interaction 12.5K views · 118 likes · 7 reposts · 3 replies Open on X →
The four essentials of daily life are clothing, food, housing, and transportation. Clothing may be the first category that home robots truly crack. Congrats to Sunday Robotics for the ACT-2 launch that got 99% accuracy in the laundry folding task! 5.4K views · 34 likes · 4 reposts · 2 replies Open on X →
Introducing ACT-2 Preview The first robotics model to unify broad generalization with high reliability. A single fine-tuning example can teach Memo a new behavior that generalizes. Zero shot, real unseen homes, 99% success rate. https://t.co/Dzj56baDtp
2:25
1M views · 4.1K likes · 482 reposts · 212 replies Open on X →
The (online) imitation learning / behavior cloning for Codex is here. 3.9K views · 20 likes · 1 reposts · 2 replies Open on X →

Against accounts of the same size

5 posts from the last 90 days, next to the 10K–100K follower range. shown widely, but few of those viewers react.

Median views12 515this account924median for 10K–100K
Reach, %110.97%this account3.62%median for 10K–100K
Engagement, %0.96%this account1.52%median for 10K–100K
MetricThis accountMedian for 10K–100KRatio
Median views per post12 51592413.5×
Reach (views ÷ followers)110.97%3.62%30.6×
Engagement rate0.96%1.52%0.63×

Others in this range →   Compare with another account →   How these benchmarks are built →

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

3.9K19 Jun
1M16 Jul
5.4K17 Jul
12.5K19 Aug
196.6K7 Sep
11.4K8 Sep
88.7K10 Sep
4K

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

0.59%19 Jun
0.48%16 Jul
0.74%17 Jul
1.02%19 Aug
0.48%7 Sep
0.96%8 Sep
0.44%10 Sep
1.05%

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

What the audience does

Likes60.4%5 410 in total
Reposts7.2%647 in total
Replies3.4%306 in total
Quotes3.7%331 in total
Bookmarks25.2%2 256 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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