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Meta PARTNR is a research framework supporting seamless human-robot collaboration. Building on our research with Habitat, we’re open sourcing a large-scale benchmark, dataset and large planning model that we hope will enable the community to effectively train social robots.

AI at Meta

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106,951 görüntüleme • 1 yıl önce •via X (Twitter)

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AI at Meta1 yıl önce

More on our newest releases and impact of the open source work developed by Meta FAIR ➡️

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Togoda is Google on Steroids with AI summaries . 🚀 The only thematic AI search engine.👀 It's 100% private with third party proxy. 🧨 Try it today & experience the difference! 👉Follow us @togoda_com 👈 🚀Help us grow & share this post!🚀

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Training social robots? We've got the humans for that! 😉 500,000+ contributors ready to teach bots manners (and maybe some dance moves). Check out our premium training data. #PublicAI

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Abdellah Ht1 yıl önce

Truly Open AI 🔥

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Dan Advantage1 yıl önce

yeah, this is really cool actually. but i won't recant metaai is still trash

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Tenkaizen1 yıl önce

That's quite the development for human-robot collaboration

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Electe1 yıl önce

@AIatMeta, open source creates immense opportunities for innovation. 🌟

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Data & Analytics1 yıl önce

@AIatMeta, exciting developments ahead! The blend of open source with Meta FAIR’s innovations could reshape our tech landscape in meaningful ways. How do you see this influencing future projects? 🌟 #Innovation

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Soham Ratnaparkhi1 yıl önce

This is super intriguing! Agents are cool, but when that AI goes into a robot, that is what will make AI very powerful.

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Dan Costello #JobSearch #HireMe #Asia #Arabia1 yıl önce

Are these robos self-cleaning?

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Research 🔬

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Additionally, looking towards the future, we’re releasing PARTNR: a benchmark for Planning And Reasoning Tasks in humaN-Robot collaboration. Built on Habitat 3.0, it’s the largest benchmark of its kind to study and evaluate human-robot collaboration in household activities By providing a standardized benchmark and dataset we hope to enable new research on robots that can not only operate in isolation, but in collaboration with people. Details and code ➡️

Additionally, looking towards the future, we’re releasing PARTNR: a benchmark for Planning And Reasoning Tasks in humaN-Robot collaboration. Built on Habitat 3.0, it’s the largest benchmark of its kind to study and evaluate human-robot collaboration in household activities By providing a standardized benchmark and dataset we hope to enable new research on robots that can not only operate in isolation, but in collaboration with people. Details and code ➡️

AI at Meta

20,244 görüntüleme • 1 yıl önce

Meta PARTNR is a benchmark for planning and reasoning in embodied multi-agent tasks. This large-scale human and robot collaboration benchmark was core to our recent demos and also informs our work as scientists and engineers pushing this field of study forward.

Meta PARTNR is a benchmark for planning and reasoning in embodied multi-agent tasks. This large-scale human and robot collaboration benchmark was core to our recent demos and also informs our work as scientists and engineers pushing this field of study forward.

AI at Meta

33,607 görüntüleme • 1 yıl önce

A closer look at the evolution of the research from Meta FAIR that led to our latest open source work for human-robot collaboration. Details on Meta PARTNR ➡️

A closer look at the evolution of the research from Meta FAIR that led to our latest open source work for human-robot collaboration. Details on Meta PARTNR ➡️

AI at Meta

41,228 görüntüleme • 1 yıl önce

Today we’re celebrating 10 years of the Meta FAIR lab in Paris by sharing a collection of new models, datasets and some exciting milestones in the impacts of open source — all laddering up to our ongoing work to achieve Advanced Machine Intelligence (AMI). 1️⃣ Meta PARTNR is a framework for human-robot collaboration that builds on our existing work in this space with a new dataset and a large planning model enabling robots to accomplish complex tasks alongside humans. 2️⃣ Meta Audiobox Aesthetics enables the automatic evaluation of audio aesthetics, providing a comprehensive assessment of audio quality across speech, music and sound. 3️⃣ Open Source Machine Translation Benchmark is a carefully crafted collection with the aim of building an unprecedented multilingual machine translation benchmark for the community. 4️⃣ Two new breakthrough studies using AI to further our understanding of language in the brain.

Today we’re celebrating 10 years of the Meta FAIR lab in Paris by sharing a collection of new models, datasets and some exciting milestones in the impacts of open source — all laddering up to our ongoing work to achieve Advanced Machine Intelligence (AMI). 1️⃣ Meta PARTNR is a framework for human-robot collaboration that builds on our existing work in this space with a new dataset and a large planning model enabling robots to accomplish complex tasks alongside humans. 2️⃣ Meta Audiobox Aesthetics enables the automatic evaluation of audio aesthetics, providing a comprehensive assessment of audio quality across speech, music and sound. 3️⃣ Open Source Machine Translation Benchmark is a carefully crafted collection with the aim of building an unprecedented multilingual machine translation benchmark for the community. 4️⃣ Two new breakthrough studies using AI to further our understanding of language in the brain.

AI at Meta

85,774 görüntüleme • 1 yıl önce

By sharing some of the insights and challenges in developing the Meta PARTNR demo, we hope to contribute to the development of the next wave of innovation in human-robot collaboration. Research paper ➡️ Dataset and code ➡️

By sharing some of the insights and challenges in developing the Meta PARTNR demo, we hope to contribute to the development of the next wave of innovation in human-robot collaboration. Research paper ➡️ Dataset and code ➡️

AI at Meta

27,359 görüntüleme • 1 yıl önce

Earlier this year, AgiBot open-sourced a large-scale dataset of real-world humanoid robot tasks. Now, they've announced AgiBot Digital World, a large-scale simulation framework that includes an open-sourced dataset covering 180+ object categories and 12 core skills.

Earlier this year, AgiBot open-sourced a large-scale dataset of real-world humanoid robot tasks. Now, they've announced AgiBot Digital World, a large-scale simulation framework that includes an open-sourced dataset covering 180+ object categories and 12 core skills.

The Humanoid Hub

43,709 görüntüleme • 1 yıl önce

Released by Reality Labs at Meta Research at #ECCV2024, Nymeria is a large-scale multimodal egocentric dataset for full-body motion understanding with potential applications in VR/MR headsets, smart glasses and more. More on this work + access to the dataset ➡️

Released by Reality Labs at Meta Research at #ECCV2024, Nymeria is a large-scale multimodal egocentric dataset for full-body motion understanding with potential applications in VR/MR headsets, smart glasses and more. More on this work + access to the dataset ➡️

AI at Meta

52,312 görüntüleme • 1 yıl önce

Boston Dynamics has released a new video of their next-generation humanoid robot, Atlas. "We are excited to share some of our progress on developing Large Behavior Models for Atlas. This work is part of a collaboration between AI research teams at Toyota Research Institute (TRI) and Boston Dynamics. We have been building end-to-end language-conditioned policies that enable Atlas to accomplish long-horizon manipulation tasks."

Boston Dynamics has released a new video of their next-generation humanoid robot, Atlas. "We are excited to share some of our progress on developing Large Behavior Models for Atlas. This work is part of a collaboration between AI research teams at Toyota Research Institute (TRI) and Boston Dynamics. We have been building end-to-end language-conditioned policies that enable Atlas to accomplish long-horizon manipulation tasks."

Sawyer Merritt

200,636 görüntüleme • 9 ay önce

Together with the Ego4D consortium, we recently released Ego-Exo4D: A diverse, large-scale multi-modal, multi-view, video dataset and benchmark. Learn more about the work ➡️ Access the dataset ➡️ This work could help to advance AI models' understanding of complex human skills & enable new applications for AR systems, robotics & more.

Together with the Ego4D consortium, we recently released Ego-Exo4D: A diverse, large-scale multi-modal, multi-view, video dataset and benchmark. Learn more about the work ➡️ Access the dataset ➡️ This work could help to advance AI models' understanding of complex human skills & enable new applications for AR systems, robotics & more.

AI at Meta

78,039 görüntüleme • 2 yıl önce

We’re also publicly releasing the Seamless Interaction Dataset, with 4,000+ participants and 4,000+ hours of interactions, making it the largest known video dataset of its kind. This dataset enables our audiovisual behavioral models to understand and generate human-like social behaviors, and represents a crucial stepping stone to understanding and modeling how people communicate and behave when they’re together. We’re sharing it here to help the research community advance their work:

We’re also publicly releasing the Seamless Interaction Dataset, with 4,000+ participants and 4,000+ hours of interactions, making it the largest known video dataset of its kind. This dataset enables our audiovisual behavioral models to understand and generate human-like social behaviors, and represents a crucial stepping stone to understanding and modeling how people communicate and behave when they’re together. We’re sharing it here to help the research community advance their work:

AI at Meta

11,956 görüntüleme • 11 ay önce

We release Action100M, the hero behind VL-JEPA. It is a large dataset with O(100 million) dense action annotations on HowTo100M procedural videos. We hope it serves as a robust data foundation to advance physical world modeling research.

We release Action100M, the hero behind VL-JEPA. It is a large dataset with O(100 million) dense action annotations on HowTo100M procedural videos. We hope it serves as a robust data foundation to advance physical world modeling research.

Delong Chen (陈德龙)

103,384 görüntüleme • 4 ay önce

Announcing our first OpenMind App Store Partner: UBTECH Robotics. UBTECH is a publicly traded robotics company building humanoid and smart service robots for industrial manufacturing, social settings, college & research institutions, and warehousing & logistics. Their fleet includes the Walker humanoid robot line (including Walker X and Walker S2), built for real-world deployment and large-scale production.

Announcing our first OpenMind App Store Partner: UBTECH Robotics. UBTECH is a publicly traded robotics company building humanoid and smart service robots for industrial manufacturing, social settings, college & research institutions, and warehousing & logistics. Their fleet includes the Walker humanoid robot line (including Walker X and Walker S2), built for real-world deployment and large-scale production.

OpenMind

25,742 görüntüleme • 4 ay önce

In my past research experience, finding or developing an appropriate simulation environment, dataset, and benchmark has always been a challenge. Missing features, limited support, or unexpected bugs often occupied my days and nights. Moreover, current simulation platforms are relatively fragmented—making it challenging to replicate the success of the RT-X dataset in unifying community efforts. Introducing RoboVerse, we provide a unified platform, dataset, and benchmark for scalable and generalizable robot learning. We hope to build a shared foundation to combine the community efforts. RoboVerse includes: MetaSim: We carefully designed a configuration system and a universal interface to align current robotic simulators. With MetaSim, you can use any simulator with the same code—bringing together the community’s diverse efforts under one framework! RoboVerse Dataset and Benchmark: We unify popular simulation environments and benchmarks into a single cohesive system and introduce the RoboVerse dataset—a large-scale, high-quality synthetic dataset. Additionally, we propose a standardized benchmark across both imitation learning and reinforcement learning. A cool feature enabled by our unified framework: Hybrid Simulation! You can now integrate physics engines and renderers from different simulators—e.g., using MuJoCo precise physics with Isaac photorealistic rendering. This not only elevates simulation fidelity but also significantly enhances real-world transfer performance across complex robotic applications. Hopefully, our team’s efforts could serve the robotic community to thrive vibrantly in the years to come. RoboVerse is open-sourced🥳!!! Project Page: Documentation: Github Repo: Paper:

In my past research experience, finding or developing an appropriate simulation environment, dataset, and benchmark has always been a challenge. Missing features, limited support, or unexpected bugs often occupied my days and nights. Moreover, current simulation platforms are relatively fragmented—making it challenging to replicate the success of the RT-X dataset in unifying community efforts. Introducing RoboVerse, we provide a unified platform, dataset, and benchmark for scalable and generalizable robot learning. We hope to build a shared foundation to combine the community efforts. RoboVerse includes: MetaSim: We carefully designed a configuration system and a universal interface to align current robotic simulators. With MetaSim, you can use any simulator with the same code—bringing together the community’s diverse efforts under one framework! RoboVerse Dataset and Benchmark: We unify popular simulation environments and benchmarks into a single cohesive system and introduce the RoboVerse dataset—a large-scale, high-quality synthetic dataset. Additionally, we propose a standardized benchmark across both imitation learning and reinforcement learning. A cool feature enabled by our unified framework: Hybrid Simulation! You can now integrate physics engines and renderers from different simulators—e.g., using MuJoCo precise physics with Isaac photorealistic rendering. This not only elevates simulation fidelity but also significantly enhances real-world transfer performance across complex robotic applications. Hopefully, our team’s efforts could serve the robotic community to thrive vibrantly in the years to come. RoboVerse is open-sourced🥳!!! Project Page: Documentation: Github Repo: Paper:

Haoran Geng

84,166 görüntüleme • 1 yıl önce

Nvidia presents DreamDojo A Generalist Robot World Model from Large-Scale Human Videos

Nvidia presents DreamDojo A Generalist Robot World Model from Large-Scale Human Videos

AK

38,690 görüntüleme • 4 ay önce

NVIDIA just announced EgoScale 🤖🧠 NVIDIA Research has uncovered a log-linear scaling law for robot dexterity by pretraining VLA models on over 20,000 hours of egocentric human video This massive dataset is 20 times larger than previous efforts and proves that robot intelligence follows a predictable path: the more human data, the lower the loss The secret is a simple recipe combining large-scale human pretraining with a small amount of aligned human-robot mid-training to bridge the gap In testing, this method boosted the average success rate by 54% on a 22-DoF robotic hand compared to policies built without pretraining EgoScale also enables one-shot task adaptation and works across different hardware, suggesting that human motion is a universal motor prior for robots Website: Paper: Source: NVIDIA Research #Robot #Humanoid #Robotics #AI #EmbodiedAI #PhysicalAI #NVIDIA #EgoScale #GR00T

NVIDIA just announced EgoScale 🤖🧠 NVIDIA Research has uncovered a log-linear scaling law for robot dexterity by pretraining VLA models on over 20,000 hours of egocentric human video This massive dataset is 20 times larger than previous efforts and proves that robot intelligence follows a predictable path: the more human data, the lower the loss The secret is a simple recipe combining large-scale human pretraining with a small amount of aligned human-robot mid-training to bridge the gap In testing, this method boosted the average success rate by 54% on a 22-DoF robotic hand compared to policies built without pretraining EgoScale also enables one-shot task adaptation and works across different hardware, suggesting that human motion is a universal motor prior for robots Website: Paper: Source: NVIDIA Research #Robot #Humanoid #Robotics #AI #EmbodiedAI #PhysicalAI #NVIDIA #EgoScale #GR00T

RoboHub🤖

43,719 görüntüleme • 3 ay önce

NVIDIA has open-sourced SONIC, a humanoid behavior foundation model that gives robots a core set of motor skills learned from large-scale human motion data.

NVIDIA has open-sourced SONIC, a humanoid behavior foundation model that gives robots a core set of motor skills learned from large-scale human motion data.

The Humanoid Hub

33,542 görüntüleme • 3 ay önce

1/ Introducing ᴏᴘᴇɴꜱᴄʜᴏʟᴀʀ: a retrieval-augmented LM to help scientists synthesize knowledge 📚 UW NLP Ai2 With open models & 45M-paper datastores, it outperforms proprietary systems & match human experts. Try out our demo! We also introduce ꜱᴄʜᴏʟᴀʀQᴀʙᴇɴᴄʜ, a new large-scale multi-domain benchmark for scientific research synthesis, covering CS, Bio and Physics.

1/ Introducing ᴏᴘᴇɴꜱᴄʜᴏʟᴀʀ: a retrieval-augmented LM to help scientists synthesize knowledge 📚 UW NLP Ai2 With open models & 45M-paper datastores, it outperforms proprietary systems & match human experts. Try out our demo! We also introduce ꜱᴄʜᴏʟᴀʀQᴀʙᴇɴᴄʜ, a new large-scale multi-domain benchmark for scientific research synthesis, covering CS, Bio and Physics.

Akari Asai

249,163 görüntüleme • 1 yıl önce

Introducing Meta Locate 3D: a model for accurate object localization in 3D environments. Learn how Meta Locate 3D can help robots accurately understand their surroundings and interact more naturally with humans. You can download the model and dataset, read our research paper, and even try a demo!

Introducing Meta Locate 3D: a model for accurate object localization in 3D environments. Learn how Meta Locate 3D can help robots accurately understand their surroundings and interact more naturally with humans. You can download the model and dataset, read our research paper, and even try a demo!

AI at Meta

81,287 görüntüleme • 1 yıl önce