正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

LingBot-VLA 2.0 is an impressive new embodied model. Open source and is trained across diverse robot configurations, from single-arm robots to humanoid platforms. It packs 60K hours of curated robot and human data into one generalist policy. It improves robots on difficult long-horizon tasks. Great release by Robbyant.

elvis

311,124 subscribers

10,464 次观看 • 3 个月前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

🤖 LingBot-VLA 2.0 is now open-source — our next-gen embodied foundation model. 🔷 60,000 hours of high-quality pretraining data — combining curated robotic demonstrations and egocentric human operation videos 🔷 20 robot configurations across 17 brands — Astribot, Leju, Unitree, Franka, Fourier, Realman, and more 🔷 Whole-body DoF: heads, waists, dexterous hands, and mobile bases — enabling far more complex task scenarios 🔷 Inference under 130ms on RTX 4090 — developer events launching soon #EmbodiedAI #Robotics #OpenSource #VLA

🤖 LingBot-VLA 2.0 is now open-source — our next-gen embodied foundation model. 🔷 60,000 hours of high-quality pretraining data — combining curated robotic demonstrations and egocentric human operation videos 🔷 20 robot configurations across 17 brands — Astribot, Leju, Unitree, Franka, Fourier, Realman, and more 🔷 Whole-body DoF: heads, waists, dexterous hands, and mobile bases — enabling far more complex task scenarios 🔷 Inference under 130ms on RTX 4090 — developer events launching soon #EmbodiedAI #Robotics #OpenSource #VLA

Robbyant

733,406 次观看 • 4 个月前

ego-to-robot data pipeline is one key component of Qwen-RobotManip （ Today, we’re sharing the full technical details behind it: Ego2Robot 🤖 It turns egocentric human videos into scalable robot training data and improves VLA generalization across tasks and embodiments. page: arxiv: huggingface upvoting 🚀:

ego-to-robot data pipeline is one key component of Qwen-RobotManip （ Today, we’re sharing the full technical details behind it: Ego2Robot 🤖 It turns egocentric human videos into scalable robot training data and improves VLA generalization across tasks and embodiments. page: arxiv: huggingface upvoting 🚀:

barry (xiong-hui) chen

17,499 次观看 • 3 个月前

The problem with humanoid teleoperation is that it is expensive and difficult to scale Enter NVIDIA's EgoScale: - A VLA model pretrained on thousands hours of egocentric human videos. - Mid-trained via 50 hours of human + 4 hours of robot "play" data for human-robot alignment. - Fine-tuned with very few examples of task-specific robot teleoperation (100 or fewer per task). - Successfully transfers across 5-finger (Sharpa) and 3-finger (Unitree G1) robot hands. - Performance scales predictably as data increases.

The problem with humanoid teleoperation is that it is expensive and difficult to scale Enter NVIDIA's EgoScale: - A VLA model pretrained on thousands hours of egocentric human videos. - Mid-trained via 50 hours of human + 4 hours of robot "play" data for human-robot alignment. - Fine-tuned with very few examples of task-specific robot teleoperation (100 or fewer per task). - Successfully transfers across 5-finger (Sharpa) and 3-finger (Unitree G1) robot hands. - Performance scales predictably as data increases.

The Humanoid Hub

44,991 次观看 • 8 个月前

🚀 We’re excited to announce LingBot-VA, a new state-of-the-art robot policy model from Robbyant ! LingBot-VA is built on a causal, autoregressive video-action world model for generalist robot control. Highlights: (1) First unified autoregressive video-action world model for robot control (2) Low-latency inference with a new asynchronous execution pipeline (3) SOTA on RoboTwin (92.9%, firstever > 90%) and LIBERO (98.5%) (4) +20% over π0.5 on challenging real-world long-horizon & high-precision tasks

🚀 We’re excited to announce LingBot-VA, a new state-of-the-art robot policy model from Robbyant ! LingBot-VA is built on a causal, autoregressive video-action world model for generalist robot control. Highlights: (1) First unified autoregressive video-action world model for robot control (2) Low-latency inference with a new asynchronous execution pipeline (3) SOTA on RoboTwin (92.9%, firstever > 90%) and LIBERO (98.5%) (4) +20% over π0.5 on challenging real-world long-horizon & high-precision tasks

Yinghao Xu

47,815 次观看 • 9 个月前

What happens when robot world models learn from human experience at scale? 🤔 DreamDojo from NVIDIA Research is a generalist robot world model pretrained on 44K hours of egocentric human videos and then post-trained on robot data to generalize across new objects and environments. After distillation, it runs at 10 FPS for live teleoperation, policy evaluation, and model-based planning. Read the ICML paper to learn more 📄

What happens when robot world models learn from human experience at scale? 🤔 DreamDojo from NVIDIA Research is a generalist robot world model pretrained on 44K hours of egocentric human videos and then post-trained on robot data to generalize across new objects and environments. After distillation, it runs at 10 FPS for live teleoperation, policy evaluation, and model-based planning. Read the ICML paper to learn more 📄

NVIDIA Robotics

23,580 次观看 • 4 个月前

World Model meets robot policy! Robbyant's LingBot-VA: unifies video world modeling and robotic policy learning. - A single model generates both future video and the actions to make it real. - Long-term memory enables long-horizon tasks. - Claims significant outperformance over π₀.₅ in real-world tasks. - It's open-source

World Model meets robot policy! Robbyant's LingBot-VA: unifies video world modeling and robotic policy learning. - A single model generates both future video and the actions to make it real. - Long-term memory enables long-horizon tasks. - Claims significant outperformance over π₀.₅ in real-world tasks. - It's open-source

The Humanoid Hub

17,721 次观看 • 9 个月前

Humanoid Robots Are Entering China’s Community Pharmacies When an online order arrives, the humanoid robot autonomously locates and navigates to the shelf, picks up the medicine, and delivers it to the packing station. The store clerk then packs it for the customer. This is a real deployment at a Guoda Pharmacy in Shanghai’s Pudong district. They’ve introduced Ant’s Robbyant wheeled humanoid robot R2, powered by the newly released LingBot VLA 2.0 + LingBot Depth, and integrated with online ordering platforms such as Meituan, Guoda APP(the brand)… Customers get their medicine faster, while pharmacists and staff have more time for professional medication consultations--especially valuable during night shifts when manpower is limited. Robbyant plans to deploy these robots in 20–50 pharmacies this year and expand to 300 stores by 2027. --- The real-world deployment flywheel --where actual usage generates more data to strengthen the models--is spinning nicely here.

CyberRobo

14,224 次观看 • 3 个月前

Tired of teleoperating your robots? We built a way to scale robot datasets without teleop, dynamic simulation, or even robot hardware. Just one smartphone scan + one human hand demo video → thousands of diverse robot trajectories. Trainable by diffusion policy and VLA models as-is. Introducing: Real2Render2Real 👉

Tired of teleoperating your robots? We built a way to scale robot datasets without teleop, dynamic simulation, or even robot hardware. Just one smartphone scan + one human hand demo video → thousands of diverse robot trajectories. Trainable by diffusion policy and VLA models as-is. Introducing: Real2Render2Real 👉

Max Fu

70,619 次观看 • 1 年前

Real-world robot data is expensive and slow to collect, creating a major challenge for humanoid development. 🤖 The NVIDIA GR00T N1.6 open vision language action model is pre-trained on a diverse mix of data, including thousands of hours of Stanford Vision and Learning Lab’s BEHAVIOR simulation data, which covers long-horizon everyday manipulation tasks. This diverse training is the key to robust cross-embodiment performance and real-world adaptability. 🌍 Read the blog 🔗

Real-world robot data is expensive and slow to collect, creating a major challenge for humanoid development. 🤖 The NVIDIA GR00T N1.6 open vision language action model is pre-trained on a diverse mix of data, including thousands of hours of Stanford Vision and Learning Lab’s BEHAVIOR simulation data, which covers long-horizon everyday manipulation tasks. This diverse training is the key to robust cross-embodiment performance and real-world adaptability. 🌍 Read the blog 🔗

NVIDIA Robotics

13,562 次观看 • 9 个月前

Ropedia just released W0, an open world model trained on ~10,000 hours of egocentric human video. One set of weights predicts first-person views for humans, robot arms, and humanoids (Unitree G1, Fourier GR-1) using a shared camera + skeleton representation. Adding a new robot only requires its URDF. It leads Cosmos 3 and DreamDojo across every benchmark tested. After distillation it runs at 21.3 FPS, enough for real-time VR control, and can roll out minute-long continuous loco-manipulation. Human pretraining transfers: the model with zero robot data already beats robot-trained baselines on several humanoid tests. In practice it can rank policies, pick better actions at runtime, and transfer to new robots without large-scale data collection. Paper, code, weights, and demos are all out.

Ropedia just released W0, an open world model trained on ~10,000 hours of egocentric human video. One set of weights predicts first-person views for humans, robot arms, and humanoids (Unitree G1, Fourier GR-1) using a shared camera + skeleton representation. Adding a new robot only requires its URDF. It leads Cosmos 3 and DreamDojo across every benchmark tested. After distillation it runs at 21.3 FPS, enough for real-time VR control, and can roll out minute-long continuous loco-manipulation. Human pretraining transfers: the model with zero robot data already beats robot-trained baselines on several humanoid tests. In practice it can rank policies, pick better actions at runtime, and transfer to new robots without large-scale data collection. Paper, code, weights, and demos are all out.

The Humanoid Hub

10,446 次观看 • 15 天前

A humanoid robot was shown packing clothes using an automatic machine by the company Robros. It picks up folded garments and places them into the machine for sealing and packaging. The robot is remotely controlled by a human, showing how robots can assist in factory packing tasks.

A humanoid robot was shown packing clothes using an automatic machine by the company Robros. It picks up folded garments and places them into the machine for sealing and packaging. The robot is remotely controlled by a human, showing how robots can assist in factory packing tasks.

Space and Technology

12,352 次观看 • 6 个月前

How well do egocentric human videos scale? Apparently, really well. Dyna Robotics has introduced Dyna-2, a world-action model (WAM) pre-trained on more than 1 million hours of real egocentric human video. It learns by jointly predicting the next video frames and the actions, and that video prediction (world modeling) is what carries learning from humans to robots. • More human video predictably improves action prediction. • Task accuracy improves across embodiments (bimanual arms and a humanoid) with zero robot data in pre-training. • Post-trained on just a few hours of robot data, mean task performance rose 20% to 53% as human pre-training scaled 1k to 1M hours. WAM vs VLA, matched apple-to-apple: the WAM hit 1.55x the VLA's success rate and won 65% of head-to-heads. Deployed zero-shot at unseen customer sites, it passed 87% vs the VLA's 46%.

How well do egocentric human videos scale? Apparently, really well. Dyna Robotics has introduced Dyna-2, a world-action model (WAM) pre-trained on more than 1 million hours of real egocentric human video. It learns by jointly predicting the next video frames and the actions, and that video prediction (world modeling) is what carries learning from humans to robots. • More human video predictably improves action prediction. • Task accuracy improves across embodiments (bimanual arms and a humanoid) with zero robot data in pre-training. • Post-trained on just a few hours of robot data, mean task performance rose 20% to 53% as human pre-training scaled 1k to 1M hours. WAM vs VLA, matched apple-to-apple: the WAM hit 1.55x the VLA's success rate and won 65% of head-to-heads. Deployed zero-shot at unseen customer sites, it passed 87% vs the VLA's 46%.

The Humanoid Hub

15,988 次观看 • 2 个月前

Training robots for the open world needs diverse data But collecting robot demos in the wild is hard! Presenting DexWild 🙌🏕️ Human data collection system that works in diverse environments, without robots 💪🦾 Human + Robot Cotraining pipeline that unlocks generalization 🧵👇

Training robots for the open world needs diverse data But collecting robot demos in the wild is hard! Presenting DexWild 🙌🏕️ Human data collection system that works in diverse environments, without robots 💪🦾 Human + Robot Cotraining pipeline that unlocks generalization 🧵👇

Tony Tao

97,762 次观看 • 1 年前

Happy to share what I’ve been working on since joining Genesis! GENE-26.5 is a one-of-a-kind, robotics-native multimodal foundation model that learns from diverse, in-the-wild data across modalities and outputs actions enabling a 54-DoF robot system to perform the most dexterous, long-horizon manipulation tasks to date—approaching human-level capability. This is the result of innovations across the full stack—data collection and processing, robot systems, model architecture, training strategies, and scalable evaluation infrastructure.

Happy to share what I’ve been working on since joining Genesis! GENE-26.5 is a one-of-a-kind, robotics-native multimodal foundation model that learns from diverse, in-the-wild data across modalities and outputs actions enabling a 54-DoF robot system to perform the most dexterous, long-horizon manipulation tasks to date—approaching human-level capability. This is the result of innovations across the full stack—data collection and processing, robot systems, model architecture, training strategies, and scalable evaluation infrastructure.

Zu

20,615 次观看 • 6 个月前

Robot actions can be represented as motion in pixel space. NVIDIA Research introduces Hydra-0, a generalist world model conditioned on action flow: image-plane trajectories that enable one model to learn across human hands, handheld grippers, single-arm robots and bimanual systems. Explore the project 📄

Robot actions can be represented as motion in pixel space. NVIDIA Research introduces Hydra-0, a generalist world model conditioned on action flow: image-plane trajectories that enable one model to learn across human hands, handheld grippers, single-arm robots and bimanual systems. Explore the project 📄

NVIDIA Robotics

63,161 次观看 • 2 个月前

Our robots integrate with @Virtuals_io to become your trading assistant. We’re showing how embodied AI means installing agents into robot brains, turning digital-only tasks into actions triggered by human-robot commands.

Our robots integrate with @Virtuals_io to become your trading assistant. We’re showing how embodied AI means installing agents into robot brains, turning digital-only tasks into actions triggered by human-robot commands.

OpenMind

168,886 次观看 • 10 个月前

Jason Ma reveals the first true scaling law in robotics: robots trained on 1 million hours of first-person human video predictably get better without ever seeing robot data "Yesterday we announced our new flagship robot foundation model called Dyna-2. It's very significant for the entire field for many different reasons. First of all, it's the first robot foundation model trained on at least one million hours of data. That in itself is a very challenging infrastructure challenge." "What we demonstrated is that even if the one million hours of data is entirely first-person video of humans doing manipulation tasks, we actually see scaling law for transferring to robot embodiments that the model has never seen before." "By training on more human data, we actually see predictable performance improvement on robots. That's a big deal because the biggest challenge facing robot foundation models is that we don't have enough data." "Unlike language models, there are just not enough robotics data out there, and it's very difficult to collect these robot data. Only by passively observing humans doing things can we scale this kind of data, and this is the first time we showed there is a lot of improvement that can transfer." Jason Ma Dyna Robotics

Jason Ma reveals the first true scaling law in robotics: robots trained on 1 million hours of first-person human video predictably get better without ever seeing robot data "Yesterday we announced our new flagship robot foundation model called Dyna-2. It's very significant for the entire field for many different reasons. First of all, it's the first robot foundation model trained on at least one million hours of data. That in itself is a very challenging infrastructure challenge." "What we demonstrated is that even if the one million hours of data is entirely first-person video of humans doing manipulation tasks, we actually see scaling law for transferring to robot embodiments that the model has never seen before." "By training on more human data, we actually see predictable performance improvement on robots. That's a big deal because the biggest challenge facing robot foundation models is that we don't have enough data." "Unlike language models, there are just not enough robotics data out there, and it's very difficult to collect these robot data. Only by passively observing humans doing things can we scale this kind of data, and this is the first time we showed there is a lot of improvement that can transfer." Jason Ma Dyna Robotics

MTS

18,002 次观看 • 2 个月前