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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 👇

462,872 görüntüleme • 7 ay önce •via X (Twitter)

37 Yorum

Physical Intelligence profil fotoğrafı
Physical Intelligence7 ay önce

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.

Physical Intelligence profil fotoğrafı
Physical Intelligence7 ay önce

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 recent interactions. 📜 For long horizon memory, we train the model to summarize events in text, allowing it to remember events for up to 15 min.

Physical Intelligence profil fotoğrafı
Physical Intelligence7 ay önce

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.

Physical Intelligence profil fotoğrafı
Physical Intelligence7 ay önce

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.

Physical Intelligence profil fotoğrafı
Physical Intelligence7 ay önce

For more, check out our paper: our blog post:

Brosnan Hoban profil fotoğrafı
Brosnan Hoban7 ay önce

Memory: because robots shouldn't keep burning grilled cheese forever.

Nurvai - The Data Layer for Physical AI profil fotoğrafı
Nurvai - The Data Layer for Physical AI7 ay önce

This is genius. Split memory into short-term visual state and long-term semantic abstraction. That’s what actually makes long-horizon tasks workable. More VLAs need this.

Andrew Erlanger profil fotoğrafı
Andrew Erlanger7 ay önce

You’re telling me the robot can’t open the fridge because of a memory issue?

hewaterGG profil fotoğrafı
hewaterGG7 ay önce

This is huge! Short-term visual + long-term semantic memory is exactly what's been missing in robotics. A robot that can remember where you keep the plates AND understand what a "plate" is - that's the key to general purpose robots. Grilled cheese sandwiches are just the beginning.

Bercan profil fotoğrafı
Bercan7 ay önce

Great work!

Reza Sayar profil fotoğrafı
Reza Sayar7 ay önce

Awesome work! 🔥🔥👏May I ask what made you decide to train your own video encoder, as opposed to using the available ones? 👀

90eights👊🏾🇰🇪 profil fotoğrafı
90eights👊🏾🇰🇪7 ay önce

The integration of short-term visual persistence with long-term semantic retrieval is the missing link for true embodied agency. Moving beyond reactive robotics into structured, long-horizon task execution is where the industry shifts. 🤖🧠

Charan Kumar profil fotoğrafı
Charan Kumar7 ay önce

Short-term visual memory in models is the piece that's been missing for truly embodied AI. If the model can remember what it just saw, the action loop becomes dramatically more coherent.

Prabhu profil fotoğrafı
Prabhu7 ay önce

Good one :)

BoldHyun profil fotoğrafı
BoldHyun7 ay önce

A robot making grilled cheese from scratch? The dual memory architecture combining visual and semantic memory for complex tasks is genuinely innovative. #AI

Abaka AI profil fotoğrafı
Abaka AI7 ay önce

Congrats, amazing work! Curious how you handle the boundary when tasks stretch past that, does the oldest summary get compressed again, or do you start losing context?

Faraz 🇺🇸 🚀 profil fotoğrafı
Faraz 🇺🇸 🚀7 ay önce

@lachygroom Sick - and this is just the start

Paul Katsen profil fotoğrafı
Paul Katsen7 ay önce

Great way to communicate progress.

Emerie profil fotoğrafı
Emerie7 ay önce

Beauty 🤞

kvick profil fotoğrafı
kvick7 ay önce

I cant wait to get my own robotic cockroach that feeds me grilled cheese sandwiches

Life Of Rick profil fotoğrafı
Life Of Rick7 ay önce

living this daily — short-term context per session, long-term memory across days. the gap between \

Branded Domain profil fotoğrafı
Branded Domain7 ay önce

#physical #AI #intelligence

Lureo profil fotoğrafı
Lureo7 ay önce

the memory piece is what most demos skip. anyone can get a robot to flip a burger once. getting it to remember where it put the spatula 30 seconds ago is the actual hard problem

Jake profil fotoğrafı
Jake7 ay önce

so cool

Ben Butler profil fotoğrafı
Ben Butler7 ay önce

v cool

The Godfather profil fotoğrafı
The Godfather7 ay önce

Cool shit.

Nico de Ory profil fotoğrafı
Nico de Ory7 ay önce

The future is closer than we think

Ziji Lozzo profil fotoğrafı
Ziji Lozzo7 ay önce

Remind Roxanne Bilbao to not stand up people on interviews, then saying she had an emergency, she is in the Philippines, WTF? communication and timing is horrid, if wanting to pay $25/hr for a teleop position at least make hiring experience better. you’re definitely a cheapskate

Mezba | NeuraLogical․com profil fotoğrafı
Mezba | NeuraLogical․com7 ay önce

PI powered Physical Intelligent

goodgoy profil fotoğrafı
goodgoy7 ay önce

and it cant vote left and file a divorce

Prasanth profil fotoğrafı
Prasanth7 ay önce

@grok is this company on the markets?

Flo | GameDev Analysis 🔜 Gamescom profil fotoğrafı
Flo | GameDev Analysis 🔜 Gamescom7 ay önce

It's time to set RFID/QR Codes on products everywhere to ease store management.

State Labs profil fotoğrafı
State Labs7 ay önce

impressive

AIKeaton UK profil fotoğrafı
AIKeaton UK7 ay önce

Visual + semantic memory integration addresses one of robotics' biggest bottlenecks. Is the training pipeline something you're open-sourcing?

Kenneth Rapoza profil fotoğrafı
Kenneth Rapoza7 ay önce

Cool. Where do I store this robot in my house and how much does it cost....?

polymaddy profil fotoğrafı
polymaddy7 ay önce

hey @RakshithSajjan this is what

polymaddy profil fotoğrafı
polymaddy7 ay önce

how viable is this for manufacturing industries

Benzer Videolar

New short course: Long-Term Agentic Memory with LangGraph. Learn to build an agent with long-term memory in this course developed in collaboration with taught by its Co-Founder and CEO, Harrison Chase! Personal assistance and productivity tasks have become important use cases for agents. An important feature of an AI assistant, such as a coding or calendar assistant, is its ability to keep improving over time from its experience. Agent memory is the key capability that enables this. To add memory to an agent, you must first figure out what to store and what to retrieve when it is time to use the information. Additionally, you’ll have to decide when to update the stored information. For example, you might update in each iteration loop of the agent or perform updates in the background, with a helper agent. In this course, you will learn a mental framework to build agents with long-term memory. You'll create a useful email assistant that can respond, ignore, and notify using writing, scheduling, and memory-management tools. You’ll develop your agent's memory by adding facts to its memory store, provide examples to learn the user's preferences, and optimize system prompts to evolve instructions based on previous responses. In detail, you’ll: - Learn how the three types of memory--semantic, episodic, and procedural–and the two update mechanisms–via hot path and in the background–apply to your agents. - Build an email agent with writing, scheduling, and availability tools, along with a router that triages incoming email and handles it accordingly by ignoring, responding, or notifying the user. - Add tools to your email agent that allow it to operate on semantic memory by learning facts about the user, storing them in a long-term memory store, and searching over them in future interactions. - Incorporate episodic memory, in the form of few-shot examples, in the triage step of your agents to help them learn and update user preferences. - Add procedural memory as system prompts, optimized with feedback to improve the instructions the agent follows. Learn how to approach memory in agents, and start building agents with long-term memory with LangGraph! Please sign up here:

Andrew Ng

132,273 görüntüleme • 1 yıl önce