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Traditional tokenization methods for robotic actions struggle with high-frequency, dexterous tasks due to redundancy and inefficiency. Inspired by JPEG compression, Physical Intelligence has developed a compressed action representation that accelerates VLA model training 5x.

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The Humanoid Hub

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26,198 Aufrufe • vor 1 Jahr •via X (Twitter)

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Profilbild von The Humanoid Hub
The Humanoid Hubvor 1 Jahr

More details in this thread:

Profilbild von UserInterface
UserInterfacevor 2 Jahren

Unveiling the Future of Prompt Engineering for Better AI Interactions #tech

Profilbild von navuud
navuudvor 1 Jahr

How does one build a laundry robot like this at home 😁

Profilbild von Brian Bellia
Brian Belliavor 1 Jahr

So, does this mean the end of teleoperation as a means of training humanoids? I hope it spells the end of teleoperation, period. At this stage, it should be autonomous or nothing - except in rare cases like Optimus catching a ball.

Profilbild von The Humanoid Hub
The Humanoid Hubvor 1 Jahr

The DROID dataset they using for training is generated with teleop

Profilbild von Disarm.AGI.UBI
Disarm.AGI.UBIvor 1 Jahr

Give'm some practice. They will get better.

Profilbild von Rethynk AI
Rethynk AIvor 1 Jahr

Really like the way it put the 2nd on the top of the first. After intellectual capital, machines are getting better understanding of physical environment.

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Anyone skeptical about AI’s ability to perform diverse physical tasks should watch this. Physical Intelligence's π₀ model in action: 18 minutes of bimanual robots autonomously handling complex, dexterous chores.
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Really excited to share what I've been working on with my colleagues at Physical Intelligence! We've developed a prototype robotic foundation model that can fold laundry, assemble a box, bus a table, and many other things. We've written a paper and blog post about it. 🧵👇
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Really excited to share what I've been working on with my colleagues at Physical Intelligence! We've developed a prototype robotic foundation model that can fold laundry, assemble a box, bus a table, and many other things. We've written a paper and blog post about it. 🧵👇

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Some great videos from the Gemini VLA release. These kinds of dexterous tasks require tons of coordination and interact with materials that are very hard to model in simulation. Good use of e2e learning and yet more evidence this is the right direction for robotics
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Today, we're joined by Sergey Levine, associate professor at UC Berkeley EECS and co-founder of Physical Intelligence to discuss π0 (pi-zero), a general-purpose robotic foundation model. We dig into the model architecture, which pairs a vision language model (VLM) with a diffusion-based action expert, and the model training "recipe," emphasizing the roles of pre-training and post-training with a diverse mixture of real-world data to ensure robust and intelligent robot learning. We review the data collection approach, which uses human operators and teleoperation rigs, the potential of synthetic data and reinforcement learning in enhancing robotic capabilities, and much more. We also introduce the team’s new FAST tokenizer, which opens the door to a fully Transformer-based model and significant improvements in learning and generalization. Finally, we cover the open-sourcing of π0 and future directions for their research. 🎧 / 🎥 Listen or watch the full episode on our page: 📖 CHAPTERS =============================== 00:00 - Introduction 2:14 - Physical Intelligence 3:47 - Key challenges in robotic learning 6:13 - Reinforcement learning in π0 and robotic foundation models 8:36 - π0 VLM model architecture 15:33 - π0 model recipe 18:39 - Pre-training dataset 22:47 - Post-training 24:23 - Laundry folding demo 31:32 - Scaling laws on π0 model 34:57 - FAST 40:26 - Open sourcing π0 43:37 - Other robot types 46:27 - Future directions

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