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Announcing a new Memory system for robots on Dimensional Robots in production generate thousands of hours of video, lidar, odometry, far too large to fit into your Agent context SpatialMemory2 builds a multimodal data store in latent space for your Agents Fully open source

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stash

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228,249 görüntüleme • 2 ay önce •via X (Twitter)

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Vibecode your robots on Dimensional. Simple demo of our physical agent stack running on the Unitree go2 quadruped. Developers can now program physical space & build dimensional applications via natural language. Fully open source.
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Vibecode your robots on Dimensional. Simple demo of our physical agent stack running on the Unitree go2 quadruped. Developers can now program physical space & build dimensional applications via natural language. Fully open source.

stash

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Announcing the Dimensional Residency in Shenzhen. Deploy agents into the physical world. Real customers. Real deployments. We provide robots, $10,000 of LLM credits, free housing, office space, and customer intros to jumpstart your robotics company.
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Announcing the Dimensional Residency in Shenzhen. Deploy agents into the physical world. Real customers. Real deployments. We provide robots, $10,000 of LLM credits, free housing, office space, and customer intros to jumpstart your robotics company.

stash

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Someone just put OpenClaw inside a Unitree G1 humanoid — and it walks. Same agent framework running on your phone. Now integrated with lidar, stereo + RGB cameras, understanding 3D space and temporal context. Works on drones and quadrupeds too. Fully open source. Cool work! Video Source: stash
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Someone just put OpenClaw inside a Unitree G1 humanoid — and it walks. Same agent framework running on your phone. Now integrated with lidar, stereo + RGB cameras, understanding 3D space and temporal context. Works on drones and quadrupeds too. Fully open source. Cool work! Video Source: stash

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3rd Place: GrokWorld uses Grok Imagine as a world model to generate synthetic training data for robots — augmenting or replacing months of manual collection in hours. Aptura AI
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3rd Place: GrokWorld uses Grok Imagine as a world model to generate synthetic training data for robots — augmenting or replacing months of manual collection in hours. Aptura AI

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[Open Source] Unitree First View Teleoperation for Humanoid Robots In order to advance the convenience of data collection for humanoid robots, we refer to other solutions to do the adaptation development and open source. Github： #Unitree #Humanoid #AGI #AI #EmbodiedIntelligence #Manipulation #Teleoperation #DataCollection
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[Open Source] Unitree First View Teleoperation for Humanoid Robots In order to advance the convenience of data collection for humanoid robots, we refer to other solutions to do the adaptation development and open source. Github： #Unitree #Humanoid #AGI #AI #EmbodiedIntelligence #Manipulation #Teleoperation #DataCollection

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We’re launching Judgment Labs today and announcing $32M in funding. As AI agents take on more of the work that creates economic value, they generate massive amounts of production data: the clearest record of how they behave with users, software, and the real world. Judgment builds infrastructure for improving AI agents from production data.
2:00

We’re launching Judgment Labs today and announcing $32M in funding. As AI agents take on more of the work that creates economic value, they generate massive amounts of production data: the clearest record of how they behave with users, software, and the real world. Judgment builds infrastructure for improving AI agents from production data.

Alex Shan

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My team is hiring in multimodal representation learning. We are working on tactile representations to equip robots with a physical understanding of the world. Bored of scraping disembodied internet data and looking for a new challenge? Apply here:
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My team is hiring in multimodal representation learning. We are working on tactile representations to equip robots with a physical understanding of the world. Bored of scraping disembodied internet data and looking for a new challenge? Apply here:

Mustafa Mukadam

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Announcing Agent Bricks: auto-optimize agents for your domain tasks. Provide a high-level description of the agent’s task, and connect your enterprise data — Agent Bricks handles the rest. Agent Bricks builds out an agent system that automatically optimizes against your goals and generates domain-specific synthetic datasets, accelerating agent development without relying on manual labeling or external sources.
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Announcing Agent Bricks: auto-optimize agents for your domain tasks. Provide a high-level description of the agent’s task, and connect your enterprise data — Agent Bricks handles the rest. Agent Bricks builds out an agent system that automatically optimizes against your goals and generates domain-specific synthetic datasets, accelerating agent development without relying on manual labeling or external sources.

Databricks

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Your Openclaw / Agent can now control multiple robots in realtime. We’ve abstracted and standardized all hardware and control interfaces so your agent can run our spatial tool calls on ANY robot. Fleet command of humanoid, quadruped, xARM, and Piper arm in unison. Fully open source.
0:35

Your Openclaw / Agent can now control multiple robots in realtime. We’ve abstracted and standardized all hardware and control interfaces so your agent can run our spatial tool calls on ANY robot. Fleet command of humanoid, quadruped, xARM, and Piper arm in unison. Fully open source.

stash

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Excited to announce RoboCasa, a large-scale simulation framework of everyday tasks! We use generative AI tools to create diverse objects, scenes, and tasks. Simulation plays a pivotal role in our Data Pyramid for training generalist robots. Open-source at
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Excited to announce RoboCasa, a large-scale simulation framework of everyday tasks! We use generative AI tools to create diverse objects, scenes, and tasks. Simulation plays a pivotal role in our Data Pyramid for training generalist robots. Open-source at

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China begins large-scale production of AI robots for warehouses.
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China begins large-scale production of AI robots for warehouses.

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Announcing Dimensional: the open-source OS for physical space. Command any robot in natural language. Program atoms, not bits.
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Announcing Dimensional: the open-source OS for physical space. Command any robot in natural language. Program atoms, not bits.

stash

193,375 görüntüleme • 8 ay önce

New short course Multimodal RAG: Chat with Videos, developed with Intel and taught by vasudevlal! In this course, you’ll work with LLaVA (Large Language and Vision Assistant), a Large Vision Language Model (LVLM) that can process both images and text. For example, given an image of a person doing a handstand on a skateboard at the beach, LLaVA doesn't just caption the scene, it’s able to predict possible outcomes, like the person losing balance or falling off. By understanding not just what's in a video frame, but what might happen next, your application can provide more insightful answers to questions about video. You'll build a full multimodal RAG pipeline that can chat about video content: - Use the BridgeTower model to create joint text-image embeddings in a 512-dimensional multimodal semantic space. - Learn video processing techniques to extract keyframes, generate transcripts using Whisper, and create captions. - Use the LanceDB vector database to store and retrieve high-dimensional multimodal embeddings. - Integrate the LLaVA model, combining CLIP's (Contrastive Language Image Pretraining) vision transformer with Llama, for advanced visual-textual reasoning. Your final system will ingest video data, generate embeddings for frames and text, perform similarity searches for relevant content, and use the retrieved multimodal context to inform LVLM-based response generation. The result is a system capable of answering nuanced questions about video content, effectively chatting about the video it has processed. Please sign up here!
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New short course Multimodal RAG: Chat with Videos, developed with Intel and taught by vasudevlal! In this course, you’ll work with LLaVA (Large Language and Vision Assistant), a Large Vision Language Model (LVLM) that can process both images and text. For example, given an image of a person doing a handstand on a skateboard at the beach, LLaVA doesn't just caption the scene, it’s able to predict possible outcomes, like the person losing balance or falling off. By understanding not just what's in a video frame, but what might happen next, your application can provide more insightful answers to questions about video. You'll build a full multimodal RAG pipeline that can chat about video content: - Use the BridgeTower model to create joint text-image embeddings in a 512-dimensional multimodal semantic space. - Learn video processing techniques to extract keyframes, generate transcripts using Whisper, and create captions. - Use the LanceDB vector database to store and retrieve high-dimensional multimodal embeddings. - Integrate the LLaVA model, combining CLIP's (Contrastive Language Image Pretraining) vision transformer with Llama, for advanced visual-textual reasoning. Your final system will ingest video data, generate embeddings for frames and text, perform similarity searches for relevant content, and use the retrieved multimodal context to inform LVLM-based response generation. The result is a system capable of answering nuanced questions about video content, effectively chatting about the video it has processed. Please sign up here!

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Agents can now understand physical space and temporality on Dimensional. Input: “Find the model airplane” With SpatioTemporal RAG, our agent has an emergent understanding of objects, stairwells, and obstacles in latent space.
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Agents can now understand physical space and temporality on Dimensional. Input: “Find the model airplane” With SpatioTemporal RAG, our agent has an emergent understanding of objects, stairwells, and obstacles in latent space.

stash

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New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!
2:57

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

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Introducing Expect Test your agent code in a real browser Works with your agent. Fully open source
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Introducing Expect Test your agent code in a real browser Works with your agent. Fully open source

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Openclaw / Agents can now command ANY robot to grasp objects via language Integrate dimensional with your personal agent to unlock the physical world Fully open source. Join the hundreds of developers already vibecoding atoms
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Openclaw / Agents can now command ANY robot to grasp objects via language Integrate dimensional with your personal agent to unlock the physical world Fully open source. Join the hundreds of developers already vibecoding atoms

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ELON: GROK TO ORCHESTRA-CONDUCT THOUSANDS OF OPTIMUS ROBOTS BUILDING FACTORIES & REFINERIES Elon just laid out the vision: Grok will act as the "orchestra conductor" directing massive teams of Optimus robots, potentially thousands, to construct huge projects like factories or rare earth refineries. “For managing a large team of Optimus robots to build a factory or a refinery, a hypothetical rare earth refinery which we desperately need in America, you start asking who’s going to manage all of that. That’s where AI comes in.” Source: Tesla
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ELON: GROK TO ORCHESTRA-CONDUCT THOUSANDS OF OPTIMUS ROBOTS BUILDING FACTORIES & REFINERIES Elon just laid out the vision: Grok will act as the "orchestra conductor" directing massive teams of Optimus robots, potentially thousands, to construct huge projects like factories or rare earth refineries. “For managing a large team of Optimus robots to build a factory or a refinery, a hypothetical rare earth refinery which we desperately need in America, you start asking who’s going to manage all of that. That’s where AI comes in.” Source: Tesla

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Can robots self-improve by collecting data autonomously🤖? Introducing SOAR: a system for large-scale autonomous data collection 🚀 and autonomous improvement📈of a multi-task language-conditioned policy in diverse scenes without human interventions .
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Can robots self-improve by collecting data autonomously🤖? Introducing SOAR: a system for large-scale autonomous data collection 🚀 and autonomous improvement📈of a multi-task language-conditioned policy in diverse scenes without human interventions .

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Can we use wearable devices to collect robot data without actual robots? Yes! With a pair of gloves🧤! Introducing DexCap, a portable hand motion capture system that collects 3D data (point cloud + finger motion) for training robots with dexterous hands Everything open-sourced

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