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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 👇

462,996 次观看 • 8 个月前 •via X (Twitter)

37 条评论

Physical Intelligence 的头像
Physical Intelligence8 个月前

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 的头像
Physical Intelligence8 个月前

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 的头像
Physical Intelligence8 个月前

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 的头像
Physical Intelligence8 个月前

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 的头像
Physical Intelligence8 个月前

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

Brosnan Hoban 的头像
Brosnan Hoban8 个月前

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

Nurvai - The Data Layer for Physical AI 的头像
Nurvai - The Data Layer for Physical AI8 个月前

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 的头像
Andrew Erlanger8 个月前

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

hewaterGG 的头像
hewaterGG8 个月前

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 的头像
Bercan8 个月前

Great work!

Reza Sayar 的头像
Reza Sayar8 个月前

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

90eights👊🏾🇰🇪 的头像
90eights👊🏾🇰🇪8 个月前

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 的头像
Charan Kumar8 个月前

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 的头像
Prabhu8 个月前

Good one :)

BoldHyun 的头像
BoldHyun8 个月前

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 的头像
Abaka AI8 个月前

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 🇺🇸 🚀 的头像
Faraz 🇺🇸 🚀8 个月前

@lachygroom Sick - and this is just the start

Paul Katsen 的头像
Paul Katsen8 个月前

Great way to communicate progress.

Emerie 的头像
Emerie8 个月前

Beauty 🤞

kvick 的头像
kvick8 个月前

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

Life Of Rick 的头像
Life Of Rick8 个月前

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

Branded Domain 的头像
Branded Domain7 个月前

#physical #AI #intelligence

Lureo 的头像
Lureo8 个月前

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 的头像
Jake8 个月前

so cool

Ben Butler 的头像
Ben Butler8 个月前

v cool

The Godfather 的头像
The Godfather8 个月前

Cool shit.

Nico de Ory 的头像
Nico de Ory8 个月前

The future is closer than we think

Ziji Lozzo 的头像
Ziji Lozzo8 个月前

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 的头像
Mezba | NeuraLogical․com8 个月前

PI powered Physical Intelligent

goodgoy 的头像
goodgoy8 个月前

and it cant vote left and file a divorce

Prasanth 的头像
Prasanth8 个月前

@grok is this company on the markets?

Flo | GameDev Analysis 🔜 Gamescom 的头像
Flo | GameDev Analysis 🔜 Gamescom8 个月前

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

State Labs 的头像
State Labs8 个月前

impressive

AIKeaton UK 的头像
AIKeaton UK8 个月前

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

Kenneth Rapoza 的头像
Kenneth Rapoza8 个月前

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

polymaddy 的头像
polymaddy8 个月前

hey @RakshithSajjan this is what

polymaddy 的头像
polymaddy8 个月前

how viable is this for manufacturing industries

相关视频

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 次观看 • 1 年前