Physical Intelligence ✓
@physical_int · San Francisco, CA · joined 11 Mar 2024
Physical Intelligence (Pi), bringing AI into the physical world.
49 437Followers
30Following
122Posts total
2MViews on collected posts
Derniers posts
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New work with @nvidia: evaluating robot policies entirely inside a world model. The policy acts, the model imagines the consequences, and the imagined evals predict real-world results. 🧵
real vs world-model rollout side by side📷 https://t.co/yR9JmyAE4G
197.6K views · 852 likes
· 115 reposts · 34 replies
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@physical_int "We are still discovering what it can do" is either the most exciting or most disturbing line in the launch because in physical systems the failure modes are a lot less recoverable than in language models.
1.8K views · 11 likes
· 0 reposts · 0 replies
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To find out more, check out our blog post, videos, and full-length research paper: https://t.co/1Kf6wRiN2P
15.5K views · 66 likes
· 3 reposts · 2 replies
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We are still discovering what π0.7 can do. It's fun to play with and the results so far have been quite surprising! https://t.co/3JDFdAHfmA
22.3K views · 96 likes
· 3 reposts · 3 replies
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π0.7 handles diverse prompts that don't just say what to do, but also how to do it, including rich language and multimodal information, such as visual subgoal images. At test time, these images can be produced by a lightweight world model. https://t.co/cbdovdVjBG
34.2K views · 106 likes
· 3 reposts · 3 replies
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Compositional generalization is a key capability of large models like LLMs, but it has been elusive in robotics. Another emergent ability we found is to control a new robot (UR5e) to fold t-shirts, even though we didn't have any laundry folding data on this robot. https://t.co/lA
14.5K views · 93 likes
· 6 reposts · 1 replies
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We are especially excited about how π0.7 seems to exhibit emergent compositional generalization: it can put together skills it learned in new ways based on the prompt, for example to figure out how to use an air fryer to cook a sweet potato. https://t.co/olnMkfmT9q
24.6K views · 118 likes
· 5 reposts · 3 replies
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Our newest model, π0.7, has some interesting emergent capabilities: it can control a new robot to fold shirts for which we had no shirt folding data, figure out how to use an appliance with language-based coaching, and perform a wide range of dexterous tasks all in one model! htt
535.3K views · 2.6K likes
· 327 reposts · 63 replies
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We developed an RL method for fine-tuning our models for precise tasks in just a few hours or even minutes. Instead of training the whole model, we add an “RL token” output to π-0.6, our latest model, which is used by a tiny actor and critic to learn quickly with RL. https://t.co
441.8K views · 2.2K likes
· 294 reposts · 39 replies
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@physical_int Memory: because robots shouldn't keep burning grilled cheese forever.
2.2K views · 17 likes
· 0 reposts · 1 replies
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For more, check out
our paper: https://t.co/xQLvOwko0m
our blog post: https://t.co/tjJO4D6MYP
12.2K views · 103 likes
· 14 reposts · 3 replies
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With MEM, our model can carry out real, multi-stage tasks in the real world, like cleaning up an entire kitchen or setting up the ingredients for a recipe. The videos show how the robot keeps track of long-horizon memory in text. https://t.co/ihCbtDUst5
7.6K views · 85 likes
· 5 reposts · 1 replies
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Apart from solving new tasks, memory also allows our policies to be more robust: we show early signs of in-context adaptation, where the robot learns to adapt its behavior on-the-fly by learning from its past mistakes. https://t.co/OBckwHuQsq
14.9K views · 98 likes
· 4 reposts · 2 replies
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Without memory, robots can struggle in many real-world situations, like failing the same way over and over again, getting stuck, or losing track of time.
But we can’t just cram minutes or hours of video into our model’s context – that would quickly become intractable. https://t.
12.6K views · 123 likes
· 8 reposts · 1 replies
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To this end, we developed Multi-Scale Embodied Memory (MEM). The key idea: use different modalities to represent memory at different time scales.
📹 For short horizon memory, we developed an efficient video encoder that lets the model remember fine-grained details about its https
28.7K views · 179 likes
· 14 reposts · 2 replies
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We’ve developed a memory system for our models that provides both short-term visual memory and long-term semantic memory.
Our approach allows us to train robots to perform long and complex tasks, like cleaning up a kitchen or preparing a grilled cheese sandwich from scratch 👇 ht
457.8K views · 2.1K likes
· 259 reposts · 48 replies
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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
7.6K3 Mar
12.2K
2.2K
441.8K19 Mar
535.3K16 Apr
24.6K
14.5K
34.2K
22.3K
15.5K
1.8K
55.4K23 Apr
86.5K3 Jun
197.6K18 Jun
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
1.21%3 Mar
0.99%
0.83%
0.59%19 Mar
0.58%16 Apr
0.53%
0.70%
0.34%
0.47%
0.46%
0.60%
0.11%23 Apr
0.23%3 Jun
0.52%18 Jun
Reactions — likes, reposts, replies and quotes — divided by views.
What the audience does
Likes63.7%9 134 in total
Reposts7.5%1 082 in total
Replies1.5%210 in total
Quotes2.1%298 in total
Bookmarks25.2%3 619 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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