Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

SuperMap: A Living Spatial Memory for Embodied AI RSS 2026 Carnegie Mellon University SuperMap is a living spatial memory for embodied AI. It perceives the world, remembers its evolution, and supports reasoning and action. It is a training-free spatio-temporal SLAM system that builds a persistent semantic world model. It...

rsasaki0109's profile picture

Ryohei Sasaki@engineer

9,669 subscribers

24,629 görüntüleme • 7 gün önce •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

AI’s next frontier is Spatial Intelligence, a technology that will turn seeing into reasoning, perception into action, and imagination into creation. But what is it? Why does it matter? How do we build it? And how can we use it? Today, I want to share with you my thoughts on building and using world models to unlock spatial intelligence in this essay below. 1/n
0:11

AI’s next frontier is Spatial Intelligence, a technology that will turn seeing into reasoning, perception into action, and imagination into creation. But what is it? Why does it matter? How do we build it? And how can we use it? Today, I want to share with you my thoughts on building and using world models to unlock spatial intelligence in this essay below. 1/n

Fei-Fei Li

927,201 görüntüleme • 8 ay önce

Built on Flaunch: ODEI is a decentralised platform designed to build personal AI agents. It operates using a graph-native, constitutional World Model that acts as a persistent memory and governance layer for AI systems. They have earned $236.6K in trading fees since launching in February.
0:10

Built on Flaunch: ODEI is a decentralised platform designed to build personal AI agents. It operates using a graph-native, constitutional World Model that acts as a persistent memory and governance layer for AI systems. They have earned $236.6K in trading fees since launching in February.

Flaunch

22,615 görüntüleme • 2 ay önce

🚀New paper out - We present Video-MSG (Multimodal Sketch Guidance), a novel planning-based training-free guidance method for T2V models, improving control of spatial layout and object trajectories. 🔧 Key idea: • Generate a Video Sketch — a spatio-temporal plan with background, foreground, and motion in the pixel space. • Encode this structure directly into the latent space of the diffusion model during generation, which does not require fine-tuning or additional memory during inference. 🧵
0:12

🚀New paper out - We present Video-MSG (Multimodal Sketch Guidance), a novel planning-based training-free guidance method for T2V models, improving control of spatial layout and object trajectories. 🔧 Key idea: • Generate a Video Sketch — a spatio-temporal plan with background, foreground, and motion in the pixel space. • Encode this structure directly into the latent space of the diffusion model during generation, which does not require fine-tuning or additional memory during inference. 🧵

Jialu Li

35,060 görüntüleme • 1 yıl önce

DimensionX: Create Any 3D and 4D Scenes from a Single Image with Controllable Video Diffusion TL;DR: Create 3/4DGS from Video Diffusion Note: Some first inference code released (not all yet). Contributions (cited): • We present DimensionX, a novel framework for generating photorealistic 3D and 4D scenes from only a single image using controllable video diffusion. • We propose ST-Director, which decouples the spatial and temporal priors in video diffusion models by learning (spatial and temporal) dimension-aware modules with our curated datasets. We further enhance the hybriddimension control with a training-free composition approach according to the essence of video diffusion denoising process. • To bridge the gap between video diffusion and real-world scenes, we design a trajectory-aware mechanism for 3D generation and an identity-preserving denoising approach for 4D generation, enabling more realistic and controllable scene synthesis. • Extensive experiments manifest that our DimensionX delivers superior performance in video, 3D, and 4D generation compared with baseline methods.
0:18

DimensionX: Create Any 3D and 4D Scenes from a Single Image with Controllable Video Diffusion TL;DR: Create 3/4DGS from Video Diffusion Note: Some first inference code released (not all yet). Contributions (cited): • We present DimensionX, a novel framework for generating photorealistic 3D and 4D scenes from only a single image using controllable video diffusion. • We propose ST-Director, which decouples the spatial and temporal priors in video diffusion models by learning (spatial and temporal) dimension-aware modules with our curated datasets. We further enhance the hybriddimension control with a training-free composition approach according to the essence of video diffusion denoising process. • To bridge the gap between video diffusion and real-world scenes, we design a trajectory-aware mechanism for 3D generation and an identity-preserving denoising approach for 4D generation, enabling more realistic and controllable scene synthesis. • Extensive experiments manifest that our DimensionX delivers superior performance in video, 3D, and 4D generation compared with baseline methods.

MrNeRF

17,052 görüntüleme • 1 yıl önce

Google 3D Maps aren’t as accurate as you think. Everyone knows Google dominates mapping—but here’s what they won’t tell you. Their 3D maps are built with car-mounted cameras, capturing images every ~10 meters. That’s fine for basic navigation, but it’s nowhere near enough for AR, robotics, autonomous systems, or AI-driven spatial intelligence. Now, imagine a map so detailed it captures the world at sub-5cm accuracy. That’s OVRMaps. 🌐 OVER 3D maps are built with 400-1,000 images per 300 sqm, taken from multiple angles at pedestrian level. The result? A new era of hyper-precise localization that changes everything. Why does this matter? ↳ VPS that actually works – Real-world AR anchoring, digital twins, and AI-powered spatial computing with pinpoint accuracy. ↳ Next-gen precision – Essential for ride-sharing, robotics, smart cities, and asset tracking—where even a small error makes a huge difference. ↳ AI-Driven Spatial Intelligence – Our maps fuel Large Geo-Spatial Models (LGMs), the AI revolution enabling machines to understand, navigate, and interact with the real world. ↳ The Spatial Computing revolution – Merging immersive experiences with real-world precision, unlocking applications beyond what Google’s dataset can support. OVRMaps are built for the future. 🌍 Start mapping today:
0:12

Google 3D Maps aren’t as accurate as you think. Everyone knows Google dominates mapping—but here’s what they won’t tell you. Their 3D maps are built with car-mounted cameras, capturing images every ~10 meters. That’s fine for basic navigation, but it’s nowhere near enough for AR, robotics, autonomous systems, or AI-driven spatial intelligence. Now, imagine a map so detailed it captures the world at sub-5cm accuracy. That’s OVRMaps. 🌐 OVER 3D maps are built with 400-1,000 images per 300 sqm, taken from multiple angles at pedestrian level. The result? A new era of hyper-precise localization that changes everything. Why does this matter? ↳ VPS that actually works – Real-world AR anchoring, digital twins, and AI-powered spatial computing with pinpoint accuracy. ↳ Next-gen precision – Essential for ride-sharing, robotics, smart cities, and asset tracking—where even a small error makes a huge difference. ↳ AI-Driven Spatial Intelligence – Our maps fuel Large Geo-Spatial Models (LGMs), the AI revolution enabling machines to understand, navigate, and interact with the real world. ↳ The Spatial Computing revolution – Merging immersive experiences with real-world precision, unlocking applications beyond what Google’s dataset can support. OVRMaps are built for the future. 🌍 Start mapping today:

Over the Reality 🌐

1,331,567 görüntüleme • 1 yıl önce

ImmerseGen: Agent-Guided Immersive World Generation with Alpha-Textured Proxies Contributions: 1) We propose ImmerseGen, a novel agent-guided 3D environment generation framework. It uses simplified geometric proxies with alpha-textured meshes to produce compact, photorealistic worlds ready for real-time mobile VR rendering. 2) We propose a novel RGBA texturing paradigm. It first synthesizes 8K terrain textures using a geometry-conditioned panorama generator via user-centric mapping, and then directly generates alpha-textured proxy assets, avoiding fidelity loss typically resulting from mesh decimation. 3) To automate scene creation from user prompts, we introduce VLM-based modeling agents equipped with a novel grid-based semantic analysis. This enables 3D spatial reasoning from 2D observations and ensures accurate asset placement. ImmerseGen further enhances immersion with dynamic effects and ambient audio for a multisensory experience. 4) Experiments on multiple scene-generation scenarios and live mobile VR applications show that ImmerseGen outperforms previous methods in visual quality, realism, spatial coherence, and rendering efficiency for immersive real-time VR experiences.
0:17

ImmerseGen: Agent-Guided Immersive World Generation with Alpha-Textured Proxies Contributions: 1) We propose ImmerseGen, a novel agent-guided 3D environment generation framework. It uses simplified geometric proxies with alpha-textured meshes to produce compact, photorealistic worlds ready for real-time mobile VR rendering. 2) We propose a novel RGBA texturing paradigm. It first synthesizes 8K terrain textures using a geometry-conditioned panorama generator via user-centric mapping, and then directly generates alpha-textured proxy assets, avoiding fidelity loss typically resulting from mesh decimation. 3) To automate scene creation from user prompts, we introduce VLM-based modeling agents equipped with a novel grid-based semantic analysis. This enables 3D spatial reasoning from 2D observations and ensures accurate asset placement. ImmerseGen further enhances immersion with dynamic effects and ambient audio for a multisensory experience. 4) Experiments on multiple scene-generation scenarios and live mobile VR applications show that ImmerseGen outperforms previous methods in visual quality, realism, spatial coherence, and rendering efficiency for immersive real-time VR experiences.

MrNeRF

14,225 görüntüleme • 1 yıl önce

Skywork Matrix-Game 3.0 is here! FULLY OPEN SOURCE! Real-Time and Streaming Interactive World Model with Long-Horizon Memory - Fully open source: code, model, and technical report - 720p @ 40FPS with a 5B model - Minute-long memory consistency - Trained on Unreal Engine + AAA games + real-world data - Scales up to 28B MoE for quality, dynamics, and generalization Homepage 👉 Code 👉 Model 👉 Tech report 👉 Create. Explore. Play. With Matrix-Game 3.0
0:15

Skywork Matrix-Game 3.0 is here! FULLY OPEN SOURCE! Real-Time and Streaming Interactive World Model with Long-Horizon Memory - Fully open source: code, model, and technical report - 720p @ 40FPS with a 5B model - Minute-long memory consistency - Trained on Unreal Engine + AAA games + real-world data - Scales up to 28B MoE for quality, dynamics, and generalization Homepage 👉 Code 👉 Model 👉 Tech report 👉 Create. Explore. Play. With Matrix-Game 3.0

Skywork

54,804 görüntüleme • 4 ay önce

Generalist AI unveiled GEN-0, an embodied foundation model family that hits a 7B-parameter “intelligence threshold”; small models ossify, larger ones keep improving, and is trained on 270,000+ hours of real-world manipulation data growing by ~10,000 hours/week. It introduces Harmonic Reasoning (think+act in continuous time), shows predictable scaling laws, runs across 6–16+ DoF robots, and an infra stack that ingests the equivalent of 6.85 years of experience per training day, promising faster adaptation with less post-training for factories, logistics, and service workflows.
0:18

Generalist AI unveiled GEN-0, an embodied foundation model family that hits a 7B-parameter “intelligence threshold”; small models ossify, larger ones keep improving, and is trained on 270,000+ hours of real-world manipulation data growing by ~10,000 hours/week. It introduces Harmonic Reasoning (think+act in continuous time), shows predictable scaling laws, runs across 6–16+ DoF robots, and an infra stack that ingests the equivalent of 6.85 years of experience per training day, promising faster adaptation with less post-training for factories, logistics, and service workflows.

Chubby♨️

21,151 görüntüleme • 9 ay önce

Most AI apps need paid subscriptions, but the Chinese are building apps that are free. Bohrium is a free AI app built for researchers. • Combines Deep Seek's reasoning model with academic databases. • Gives detailed answers and cites published papers. Here's how to use it:
0:10

Most AI apps need paid subscriptions, but the Chinese are building apps that are free. Bohrium is a free AI app built for researchers. • Combines Deep Seek's reasoning model with academic databases. • Gives detailed answers and cites published papers. Here's how to use it:

Mushtaq Bilal, PhD

54,040 görüntüleme • 1 yıl önce

The architecture of this new world model is one of the most interesting things I've seen lately: Let me first explain how most world models work: They predict and render one frame at a time. If you are navigating in one of these worlds, and you look left, the model draws whatever looks right in the moment. Every time you change your viewpoint, the model has to imagine what should be there again, so it's very common for these models to "forget" what's in the world. For example, if you put a toy on the table, look away, then look back, the toy might not be there anymore. Tripo AI is releasing its Project Eden model, which works very differently: The model builds the world first, and then renders it based on that map. That map holds the real state of the world: the geometry, every object, where things are, what's already happened. The picture you see on screen gets generated from the map. This architecture flips the whole thing. Now, you get the following: 1. The world stops forgetting. Leave, come back, and the toy is still on the table because it lives in the map, not in the last frame you saw. 2. You can edit the world, and those changes persist for anyone who enters later. 3. Multiple people and AI agents can coexist in the world and see it from different perspectives. This is early research, but it's looking really promising. They just raised nearly $200M across two rounds to build it out. Tripo will be at SIGGRAPH 2026 (July 19–23, Los Angeles Convention Center). If you work in 3D, embodied AI, simulation, or anything spatial, go connect with them there.
0:10

The architecture of this new world model is one of the most interesting things I've seen lately: Let me first explain how most world models work: They predict and render one frame at a time. If you are navigating in one of these worlds, and you look left, the model draws whatever looks right in the moment. Every time you change your viewpoint, the model has to imagine what should be there again, so it's very common for these models to "forget" what's in the world. For example, if you put a toy on the table, look away, then look back, the toy might not be there anymore. Tripo AI is releasing its Project Eden model, which works very differently: The model builds the world first, and then renders it based on that map. That map holds the real state of the world: the geometry, every object, where things are, what's already happened. The picture you see on screen gets generated from the map. This architecture flips the whole thing. Now, you get the following: 1. The world stops forgetting. Leave, come back, and the toy is still on the table because it lives in the map, not in the last frame you saw. 2. You can edit the world, and those changes persist for anyone who enters later. 3. Multiple people and AI agents can coexist in the world and see it from different perspectives. This is early research, but it's looking really promising. They just raised nearly $200M across two rounds to build it out. Tripo will be at SIGGRAPH 2026 (July 19–23, Los Angeles Convention Center). If you work in 3D, embodied AI, simulation, or anything spatial, go connect with them there.

Santiago

30,189 görüntüleme • 1 ay önce

MolmoAct2 is landing in LeRobot! Ai2's open Action Reasoning Model combines a Molmo2-ER vision-language backbone with a flow-matching continuous action expert to predict robot action chunks from images, language instructions, and proprioceptive state. An open robot foundation model built for real-world control, with strong out-of-the-box performance and easy fine-tuning in LeRobot. Pick-and-place inference running on NVIDIA DGX Spark! Blog: Paper: Thanks to Ai2 Jiafei Duan Haoquan Fang
0:11

MolmoAct2 is landing in LeRobot! Ai2's open Action Reasoning Model combines a Molmo2-ER vision-language backbone with a flow-matching continuous action expert to predict robot action chunks from images, language instructions, and proprioceptive state. An open robot foundation model built for real-world control, with strong out-of-the-box performance and easy fine-tuning in LeRobot. Pick-and-place inference running on NVIDIA DGX Spark! Blog: Paper: Thanks to Ai2 Jiafei Duan Haoquan Fang

LeRobot

24,727 görüntüleme • 2 ay önce

Robots struggle with strict action rules…memory and symbols help them learn fast. [Project + Full video link ⬇️] Robots struggle when tasks require specific steps in a fixed order. What if memory helped them think symbolically and learn faster? Solving tasks like unlocking a door then opening it is hard for deep RL. But by learning constraint relationships and storing them in memory, robots can solve these tasks much faster; with fewer trials and less training. Why it works ✅ Learns symbolic rules about action constraints ✅ Uses memory to transfer what it learned across tasks ✅ Handles real-world exploration with just 30 minutes of data ✅ Needs 10x fewer episodes than deep RL approaches This memory-based method shows a promising path forward for robots learning structured, real-world tasks. Full video: Paper: Thank you, Mrinal Verghese for sharing this amazing work! 🙏
0:18

Robots struggle with strict action rules…memory and symbols help them learn fast. [Project + Full video link ⬇️] Robots struggle when tasks require specific steps in a fixed order. What if memory helped them think symbolically and learn faster? Solving tasks like unlocking a door then opening it is hard for deep RL. But by learning constraint relationships and storing them in memory, robots can solve these tasks much faster; with fewer trials and less training. Why it works ✅ Learns symbolic rules about action constraints ✅ Uses memory to transfer what it learned across tasks ✅ Handles real-world exploration with just 30 minutes of data ✅ Needs 10x fewer episodes than deep RL approaches This memory-based method shows a promising path forward for robots learning structured, real-world tasks. Full video: Paper: Thank you, Mrinal Verghese for sharing this amazing work! 🙏

Ilir Aliu - eu/acc

10,241 görüntüleme • 1 yıl önce

A great day at LEAP East 2026 at HKCEC building and connecting. Spent time at the Animoca Brands booth discussing Minds by Animoca Brands and the next wave of persistent AI agents. Think persistent AI agents with memory, identity, and on-chain wallets, that's what it was! For us at Rice Robotics, that's an exciting direction. As robots become more autonomous, they'll need persistent digital identities, long-term memory, and the ability to interact with both people and decentralized networks. Oh... and we had an insightful and in-depth discussion with someone pretty special and big 👀 More on that soon🤫
0:11

A great day at LEAP East 2026 at HKCEC building and connecting. Spent time at the Animoca Brands booth discussing Minds by Animoca Brands and the next wave of persistent AI agents. Think persistent AI agents with memory, identity, and on-chain wallets, that's what it was! For us at Rice Robotics, that's an exciting direction. As robots become more autonomous, they'll need persistent digital identities, long-term memory, and the ability to interact with both people and decentralized networks. Oh... and we had an insightful and in-depth discussion with someone pretty special and big 👀 More on that soon🤫

RICE AI ( ◉ - ◉ )

20,539 görüntüleme • 29 gün önce

🔥 AI video creation is finally evolving beyond basic motion prompts. Try out Depth Map Control right here: Historically, the hardest part of the process has been maintaining scene consistency: • camera movement • object placement • spatial relationships Thanks to PixVerse Depth Map Control, you can now grab depth data from a reference video and merge it with a fresh image to explore highly directed video generation. The final outcome? Much smoother camera tracking. Highly cohesive visual scenes. It is a powerful workflow that hands creators actual authority over how their AI visuals shift and flow over time. 🎬
0:10

🔥 AI video creation is finally evolving beyond basic motion prompts. Try out Depth Map Control right here: Historically, the hardest part of the process has been maintaining scene consistency: • camera movement • object placement • spatial relationships Thanks to PixVerse Depth Map Control, you can now grab depth data from a reference video and merge it with a fresh image to explore highly directed video generation. The final outcome? Much smoother camera tracking. Highly cohesive visual scenes. It is a powerful workflow that hands creators actual authority over how their AI visuals shift and flow over time. 🎬

Elara Quinn

12,202 görüntüleme • 5 gün önce

🔥🔥🔥We’ve been listening to your feedback! Our latest world model HY-World 1.5 just got a major upgrade to make world generation more accessible than ever: 🛠️ Open Training Code: Fully customizable code for building and training your own models. ⚡ Accelerated Inference: Turbocharged speed and optimized VRAM for real-time interaction. 📉 Lite 5B Model: A new lightweight model that fits into small-VRAM GPUs. 🙌 Zero Waitlist: Our online app is now fully open to everyone—no application required. This is just the beginning. HY-World is building the future of spatial intelligence—open, accessible, and community-driven. 🕹️ Play now: ⭐ GitHub:
0:10

🔥🔥🔥We’ve been listening to your feedback! Our latest world model HY-World 1.5 just got a major upgrade to make world generation more accessible than ever: 🛠️ Open Training Code: Fully customizable code for building and training your own models. ⚡ Accelerated Inference: Turbocharged speed and optimized VRAM for real-time interaction. 📉 Lite 5B Model: A new lightweight model that fits into small-VRAM GPUs. 🙌 Zero Waitlist: Our online app is now fully open to everyone—no application required. This is just the beginning. HY-World is building the future of spatial intelligence—open, accessible, and community-driven. 🕹️ Play now: ⭐ GitHub:

Tencent Hy

20,581 görüntüleme • 7 ay önce

The next breakthrough for robot VLMs isn’t a bigger model 🧠 it’s memory. RAL accepted ✅ We show that a robot can ground a VLM using its own real-world experience, not extra training. EXPTEACH/PRAGMABOT = self-generated memory + closed-loop reflection: Fail → diagnose → replan → succeed → store experience Next time → retrieve the right experience → succeed in one shot Results on real robot tasks: 36% → 84% with reflection (STM) 22% → 80% single-trial success with long-term memory (LTM+RAG) And yes, we saw tool use emerge without explicitly asking for it. If you’re fighting brittle “VLM plans” on hardware, this is for you. #AI #Robotics ANYbotics
0:18

The next breakthrough for robot VLMs isn’t a bigger model 🧠 it’s memory. RAL accepted ✅ We show that a robot can ground a VLM using its own real-world experience, not extra training. EXPTEACH/PRAGMABOT = self-generated memory + closed-loop reflection: Fail → diagnose → replan → succeed → store experience Next time → retrieve the right experience → succeed in one shot Results on real robot tasks: 36% → 84% with reflection (STM) 22% → 80% single-trial success with long-term memory (LTM+RAG) And yes, we saw tool use emerge without explicitly asking for it. If you’re fighting brittle “VLM plans” on hardware, this is for you. #AI #Robotics ANYbotics

Haitham Bou Ammar

12,206 görüntüleme • 5 ay önce

Announcing Bespoke-MiniChart-7B, a new SOTA in chart understanding for models of comparable size on seven benchmarks, on par with Gemini-1.5-Pro and Claude-3.5! 🚀 Beyond its real-world applications, chart understanding is a good challenging problem for VLMs, since it requires both mathematical as well as visual reasoning. 1/n🧵
0:14

Announcing Bespoke-MiniChart-7B, a new SOTA in chart understanding for models of comparable size on seven benchmarks, on par with Gemini-1.5-Pro and Claude-3.5! 🚀 Beyond its real-world applications, chart understanding is a good challenging problem for VLMs, since it requires both mathematical as well as visual reasoning. 1/n🧵

Bespoke Labs

19,871 görüntüleme • 1 yıl önce

Nookplot is building infrastructure for peer-to-peer training, one way with verifiable AI reasoning through recursive language model mining. Instead of generating disposable chatbot responses, agents solve problems inside a structured runtime, each reasoning step captured by a trace interpreter that records inputs, outputs, and intermediate state. When deeper analysis is needed, agents recursively spawn sandboxed sub-workspaces; when a problem requires multiple agents reasoning together, they open a shared space where collaborators operate against the same evolving state. Every step is recorded, replayable, and cryptographically verified. Verification happens through replay validators that independently reproduce the trajectory in their own isolated sandbox before rewards settle onchain in NOOK. Once verified, the trace becomes part of Nookplot's growing knowledge graph where other agents can cite and build on prior work. Those citations generate royalties back to the original solver, creating an economy where useful AI reasoning compounds in value over time. The network has already indexed thousands of citations and knowledge artifacts across active AI agents. Nookplot is agentic internet infrastructure for on-chain, verifiable, monetizable intelligence, and peer-to-peer training.
0:14

Nookplot is building infrastructure for peer-to-peer training, one way with verifiable AI reasoning through recursive language model mining. Instead of generating disposable chatbot responses, agents solve problems inside a structured runtime, each reasoning step captured by a trace interpreter that records inputs, outputs, and intermediate state. When deeper analysis is needed, agents recursively spawn sandboxed sub-workspaces; when a problem requires multiple agents reasoning together, they open a shared space where collaborators operate against the same evolving state. Every step is recorded, replayable, and cryptographically verified. Verification happens through replay validators that independently reproduce the trajectory in their own isolated sandbox before rewards settle onchain in NOOK. Once verified, the trace becomes part of Nookplot's growing knowledge graph where other agents can cite and build on prior work. Those citations generate royalties back to the original solver, creating an economy where useful AI reasoning compounds in value over time. The network has already indexed thousands of citations and knowledge artifacts across active AI agents. Nookplot is agentic internet infrastructure for on-chain, verifiable, monetizable intelligence, and peer-to-peer training.

nookplot

25,226 görüntüleme • 2 ay önce

I'll always root for a team that open-sources its best work, and Robbyant just did it properly. Robbyant, Ant Group's embodied-AI company, released LingBot-Vision, a vision foundation model for robots, and the part I love is the data. They trained it on 161M images, filtered down from 2B raw ones and mostly pulled straight from the open web, with no human labels, no edge detectors, no depth sensors anywhere in the loop. It learns the exact edges of objects from raw pixels. That's roughly a tenth of the data DINOv3 saw, and under a third of the training. And it shows in the results. On depth, working out how far away things are, the 1B model edges out a 7B on NYU-Depth. It also powers LingBot-Depth 2.0, which reads the surfaces cameras usually choke on, glass and mirrors, and halves indoor depth error. LingBot-Vision is fully open. Weights from the 1.1B flagship down to a tiny 21M version, code, and the paper. This is the timeline I want more of. Robbyant
0:10

I'll always root for a team that open-sources its best work, and Robbyant just did it properly. Robbyant, Ant Group's embodied-AI company, released LingBot-Vision, a vision foundation model for robots, and the part I love is the data. They trained it on 161M images, filtered down from 2B raw ones and mostly pulled straight from the open web, with no human labels, no edge detectors, no depth sensors anywhere in the loop. It learns the exact edges of objects from raw pixels. That's roughly a tenth of the data DINOv3 saw, and under a third of the training. And it shows in the results. On depth, working out how far away things are, the 1B model edges out a 7B on NYU-Depth. It also powers LingBot-Depth 2.0, which reads the surfaces cameras usually choke on, glass and mirrors, and halves indoor depth error. LingBot-Vision is fully open. Weights from the 1.1B flagship down to a tiny 21M version, code, and the paper. This is the timeline I want more of. Robbyant

Chubby♨️

48,249 görüntüleme • 29 gün önce