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Training data collection is one of the biggest challenges in #Robotics. We're solving it with #NVIDIAIsaac GR00T-Dreams. By using #NVIDIACosmos world foundation models and generative AI, developers can create vast, realistic synthetic data at scale. Benefits: ✔️Cheaper & faster data collection ✔️Smarter robots in new environments ✔️ No more... show more

NVIDIA Robotics

140,693 subscribers

17,037 次观看 • 1 年前 •via X (Twitter)

科学技术 #Robotics #NVIDIAIsaac #NVIDIACosmos

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8 条评论

Tanya 的头像

Tanya1 年前

Amazing to see how NVIDIA is making training data collection easier and more affordable with synthetic data. This is a real game-changer for robotics development

XerpaAI 的头像

XerpaAI1 年前

are they on sale already?

DJC 的头像

DJC1 年前

Go bots

Prime Domains (for Startups 🏆) 的头像

Prime Domains (for Startups 🏆)1 年前

Robot Industries- depends on training the data 📊

BONSYSTEMS 的头像

BONSYSTEMS1 年前

This is a technology that will greatly benefit the robotics ecosystem. It will surely serve as a valuable reference for many developers!

Extend Robotics 的头像

Extend Robotics1 年前

Setup your data pipeline with worlds most intuitive XR interface.

Anurag Pant 的头像

Anurag Pant1 年前

👍👍👍

Tomasz Proshowski 的头像

Tomasz Proshowski1 年前

Dear INVIDIA Robotics , wonderful news, congratulations 👏👏👏👏👏👏👏👏👏👏 start letting your robots of for sales in superstores this summer please. Thank you

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So I heard we need more data for robot learning :) Purely real world teleop is expensive and slow, making large scale data collection challenging. I’ve been excited about getting more data into robot learning, going beyond just real-world teleop data. To this end, we’ve been scaling up data generation with RL in realistic simulations generated on the fly from crowdsourced videos. Enables realistic data collection, much more cheaply than purely real world teleop. Importantly, data collection becomes even*cheaper* with more environments, allowing training with over 100x more data. Transfers to real robots for generalizable manipulation. A 🧵 (1/N)

So I heard we need more data for robot learning :) Purely real world teleop is expensive and slow, making large scale data collection challenging. I’ve been excited about getting more data into robot learning, going beyond just real-world teleop data. To this end, we’ve been scaling up data generation with RL in realistic simulations generated on the fly from crowdsourced videos. Enables realistic data collection, much more cheaply than purely real world teleop. Importantly, data collection becomes evencheaper with more environments, allowing training with over 100x more data. Transfers to real robots for generalizable manipulation. A 🧵 (1/N)

Abhishek Gupta

13,358 次观看 • 1 年前

It's easy for robots to nail a demo. It's much harder to get them to work reliably in the real world. The bottleneck is data. At Axis Robotics we're building a compounding data engine that combines simulation, ego-centric data and DAgger correction data at scale. Watch our CEO ChrisF(✱,✱) break down the data bottleneck, why messy data can still train good models, and what it takes to get robots out of the lab with blocmates. ⬇️

It's easy for robots to nail a demo. It's much harder to get them to work reliably in the real world. The bottleneck is data. At Axis Robotics we're building a compounding data engine that combines simulation, ego-centric data and DAgger correction data at scale. Watch our CEO ChrisF(✱,✱) break down the data bottleneck, why messy data can still train good models, and what it takes to get robots out of the lab with blocmates. ⬇️

Axis Robotics

22,634 次观看 • 24 天前

Today we're launching Sentry—the first-of- its-kind persistent monitoring solution that can simultaneously monitor activity across hundreds of sites to deliver predictive intelligence at global scale. Sentry brings together three powerful capabilities: ✔️ Seamlessly integrate tasking feasibility and scheduling across multiple satellite constellations ✔️ Automatically fuse data from diverse sensors into a fully co-registered dataset for large-scale analysis ✔️ AI/ML models compare new data to Maxar’s 250+PB archive to detect anomalous patterns and potential threats The world moves fast. Our intelligence moves faster. See it before it happens, everywhere it matters. ➡️ #SentryByMaxar #GEOINT #PersistentMonitoring #AI #ISR

Today we're launching Sentry—the first-of- its-kind persistent monitoring solution that can simultaneously monitor activity across hundreds of sites to deliver predictive intelligence at global scale. Sentry brings together three powerful capabilities: ✔️ Seamlessly integrate tasking feasibility and scheduling across multiple satellite constellations ✔️ Automatically fuse data from diverse sensors into a fully co-registered dataset for large-scale analysis ✔️ AI/ML models compare new data to Maxar’s 250+PB archive to detect anomalous patterns and potential threats The world moves fast. Our intelligence moves faster. See it before it happens, everywhere it matters. ➡️ #SentryByMaxar #GEOINT #PersistentMonitoring #AI #ISR

Vantor

11,407 次观看 • 1 年前

Today we're launching Hebbian Robotics (YC S26) Hebbian Robotics (YC S26) . We build APIs for evaluating data quality, without training a robotics model. Data vendors use our API to search and analyze Physical AI data at scale, getting on-demand quality signals and metrics without managing infrastructure. High-quality data is the bottleneck to the next frontier of intelligence in robotics. Buyers want data they can trust. Data providers are focused on operational excellence. They don't have the bandwidth to understand the quality of their data at scale. We're fixing this. Vendors use our APIs to understand the impact of their decisions on their data quality: - Buyers demanding per-collector hour limits? Prove compliance while we stay ahead of the workarounds. - Changed your collection SOPs? Monitor for quality drift and regression. - Suspect duplicate samples? Verify with a single API call. Before Hebbian, I trained AI models for industrial cleaning robots, and kingston kuan spent years building large-scale infrastructure at Jane Street.

Today we're launching Hebbian Robotics (YC S26) Hebbian Robotics (YC S26) . We build APIs for evaluating data quality, without training a robotics model. Data vendors use our API to search and analyze Physical AI data at scale, getting on-demand quality signals and metrics without managing infrastructure. High-quality data is the bottleneck to the next frontier of intelligence in robotics. Buyers want data they can trust. Data providers are focused on operational excellence. They don't have the bandwidth to understand the quality of their data at scale. We're fixing this. Vendors use our APIs to understand the impact of their decisions on their data quality: - Buyers demanding per-collector hour limits? Prove compliance while we stay ahead of the workarounds. - Changed your collection SOPs? Monitor for quality drift and regression. - Suspect duplicate samples? Verify with a single API call. Before Hebbian, I trained AI models for industrial cleaning robots, and kingston kuan spent years building large-scale infrastructure at Jane Street.

Brandon Ong

165,966 次观看 • 27 天前

Austin-based robotics startup Apptronik has unveiled Apollo 2, the latest version of its humanoid robot. Apollo 2 comes in both bipedal and wheeled versions and performs real-world tasks while collecting training data. Apptronik is using this data with Google DeepMind to train Gemini Robotics AI models, making Apollo smarter and more capable.

Austin-based robotics startup Apptronik has unveiled Apollo 2, the latest version of its humanoid robot. Apollo 2 comes in both bipedal and wheeled versions and performs real-world tasks while collecting training data. Apptronik is using this data with Google DeepMind to train Gemini Robotics AI models, making Apollo smarter and more capable.

Space and Technology

11,540 次观看 • 1 个月前

We might be solving the wrong problem in robotics. That’s what this makes clear. UMI → Universal Manipulation Interface A simple $400 gripper that lets you teach robots by demonstration. You hold it like a tool. Show the task. The robot learns. No teleoperation. No expensive hardware. No robot-specific data. Stanford open-sourced everything → hardware, code, datasets. What stands out to me is the bottleneck. Not algorithms. Data. Teleoperation → ~35 demos/hour UMI → ~111 demos/hour And the data transfers across robots → UR5, Franka, others. The design is surprisingly practical: → GoPro fisheye lens (155° FOV) + mirrors for depth → SLAM + IMU for precise 6DoF tracking → latency matching for dynamic tasks → diffusion policies for multimodal actions Then it scales. Cheng Chi takes this further with Sunday Robotics (with Tony Zhao). A $200 glove → deployed in 500+ homes → ~10 million real-world interactions. Not lab data. Real human behavior. Their robot learns dishes, laundry, espresso → with zero robot-specific data. This is where the shift becomes obvious. From training robots in controlled environments → to learning directly from humans at scale So here’s the real question: Will robotics be unlocked by better models… or by unlocking data? #ArtificialIntelligence #Robotics #AI #Innovation #FutureOfWork

We might be solving the wrong problem in robotics. That’s what this makes clear. UMI → Universal Manipulation Interface A simple $400 gripper that lets you teach robots by demonstration. You hold it like a tool. Show the task. The robot learns. No teleoperation. No expensive hardware. No robot-specific data. Stanford open-sourced everything → hardware, code, datasets. What stands out to me is the bottleneck. Not algorithms. Data. Teleoperation → ~35 demos/hour UMI → ~111 demos/hour And the data transfers across robots → UR5, Franka, others. The design is surprisingly practical: → GoPro fisheye lens (155° FOV) + mirrors for depth → SLAM + IMU for precise 6DoF tracking → latency matching for dynamic tasks → diffusion policies for multimodal actions Then it scales. Cheng Chi takes this further with Sunday Robotics (with Tony Zhao). A $200 glove → deployed in 500+ homes → ~10 million real-world interactions. Not lab data. Real human behavior. Their robot learns dishes, laundry, espresso → with zero robot-specific data. This is where the shift becomes obvious. From training robots in controlled environments → to learning directly from humans at scale So here’s the real question: Will robotics be unlocked by better models… or by unlocking data? #ArtificialIntelligence #Robotics #AI #Innovation #FutureOfWork

Pascal Bornet

186,712 次观看 • 5 个月前

Schaeffler is bringing UK's humanoid robots from concept to factory reality. Within the next 5 years, hundreds of humanoid robots will be deployed in Schaeffler's global factories, with beta versions first implemented in 2026–2027. Schaeffler is also becoming the preferred supplier of joint actuators for the humanoid wheeled platform and is jointly exploring next-generation actuator architectures with its partner. More importantly, the two companies will collaborate deeply on data and skills: continuously accumulating real-world manufacturing data through teleoperation, synthetic data, and peripheral data acquisition robots to train AI models, enabling humanoid robots to learn faster and perform more reliably in the factory.

Schaeffler is bringing UK's humanoid robots from concept to factory reality. Within the next 5 years, hundreds of humanoid robots will be deployed in Schaeffler's global factories, with beta versions first implemented in 2026–2027. Schaeffler is also becoming the preferred supplier of joint actuators for the humanoid wheeled platform and is jointly exploring next-generation actuator architectures with its partner. More importantly, the two companies will collaborate deeply on data and skills: continuously accumulating real-world manufacturing data through teleoperation, synthetic data, and peripheral data acquisition robots to train AI models, enabling humanoid robots to learn faster and perform more reliably in the factory.

CyberRobo

19,907 次观看 • 7 个月前

Jensen just announced NVIDIA’s Isaac GR00T N1.5 and GR00T-Dreams blueprint at COMPUTEX 2025: ⦿ Isaac GR00T N1.5 is the first update to NVIDIA’s open, generalized, fully customizable foundation model for humanoid reasoning and skills. ⦿ “Human demonstrations aren’t scalable — limited by the number of hours in a day,” said Jensen. The GR00T-Dreams blueprint enables the generation of vast synthetic motion data from single images, accelerating robot behavior learning via compressed action tokens. ⦿ Synthetic training data generated by GR00T-Dreams was used to develop GR00T N1.5 in just 36 hours — what would have taken nearly three months without the blueprint. ⦿ This update significantly improves the foundation model’s success rate for common material handling and manufacturing tasks. GR00T N1.5 can be deployed on Jetson Thor, launching later this year.

Jensen just announced NVIDIA’s Isaac GR00T N1.5 and GR00T-Dreams blueprint at COMPUTEX 2025: ⦿ Isaac GR00T N1.5 is the first update to NVIDIA’s open, generalized, fully customizable foundation model for humanoid reasoning and skills. ⦿ “Human demonstrations aren’t scalable — limited by the number of hours in a day,” said Jensen. The GR00T-Dreams blueprint enables the generation of vast synthetic motion data from single images, accelerating robot behavior learning via compressed action tokens. ⦿ Synthetic training data generated by GR00T-Dreams was used to develop GR00T N1.5 in just 36 hours — what would have taken nearly three months without the blueprint. ⦿ This update significantly improves the foundation model’s success rate for common material handling and manufacturing tasks. GR00T N1.5 can be deployed on Jetson Thor, launching later this year.

The Humanoid Hub

69,557 次观看 • 1 年前

Chelsea Finn (Chelsea Finn) on building general-purpose robotics, and bringing intelligence into the physical world. At AI Startup School in San Francisco. 00:00 - General Purpose Robots 00:11 - Challenges in Robotics Applications 00:57 - Physical Intelligence: A New Approach 01:47 - Learning from Language Models 02:08 - Data Sources for Training Robots 03:32 - Training with Real-World Data 04:39 - Initial Successes and Challenges 09:10 - Breakthrough in Robot Training 11:03 - Improving Performance 15:43 - Expanding Capabilities 17:34 - Robots in Unseen Environments 25:54 - Handling Open-Ended Prompts 29:36 - Evaluating Robot Performance 30:03 - Future Directions and Challenges 31:27 - Audience Q&A

Chelsea Finn (Chelsea Finn) on building general-purpose robotics, and bringing intelligence into the physical world. At AI Startup School in San Francisco. 00:00 - General Purpose Robots 00:11 - Challenges in Robotics Applications 00:57 - Physical Intelligence: A New Approach 01:47 - Learning from Language Models 02:08 - Data Sources for Training Robots 03:32 - Training with Real-World Data 04:39 - Initial Successes and Challenges 09:10 - Breakthrough in Robot Training 11:03 - Improving Performance 15:43 - Expanding Capabilities 17:34 - Robots in Unseen Environments 25:54 - Handling Open-Ended Prompts 29:36 - Evaluating Robot Performance 30:03 - Future Directions and Challenges 31:27 - Audience Q&A

Y Combinator

129,834 次观看 • 1 年前

NVIDIA just introduced Cosmos, a platform for world foundation models designed for robotics. ⦿ It features advanced tokenizers, an AI-accelerated data pipeline, and integration with NVIDIA Omniverse. Humanoid makers 1X, Figure, and Agility are among the first to adopt Cosmos. ⦿ Cosmos generates synthetic, physics-based data, accelerating model training and customization. ⦿ It also features a CUDA-accelerated data processing pipeline that enables developers to process, curate, and label 20 million hours of videos in 14 days using the NVIDIA Blackwell platform.

NVIDIA just introduced Cosmos, a platform for world foundation models designed for robotics. ⦿ It features advanced tokenizers, an AI-accelerated data pipeline, and integration with NVIDIA Omniverse. Humanoid makers 1X, Figure, and Agility are among the first to adopt Cosmos. ⦿ Cosmos generates synthetic, physics-based data, accelerating model training and customization. ⦿ It also features a CUDA-accelerated data processing pipeline that enables developers to process, curate, and label 20 million hours of videos in 14 days using the NVIDIA Blackwell platform.

The Humanoid Hub

129,481 次观看 • 1 年前

Axis is officially LIVE on Base. 🔵 Axis is scaling Physical AI for the real world, contributed by everyone. You can control robots in a virtual world, generate training data at scale, and help build the brain behind tomorrow's robots. All from browser. No hardware needed. Start building robotics intelligence today:

Axis is officially LIVE on Base. 🔵 Axis is scaling Physical AI for the real world, contributed by everyone. You can control robots in a virtual world, generate training data at scale, and help build the brain behind tomorrow's robots. All from browser. No hardware needed. Start building robotics intelligence today:

Axis Robotics

396,670 次观看 • 5 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

17,549 次观看 • 24 天前

World Labs CEO Dr. Fei-Fei Li & SceniX Co-Founder Yunzhu Li on the data bottleneck in robotics and how world models help: "The lack of data in training, the lack of data in evaluation, this is very, very different from language models, where data is abundant on the internet." "And we know that in order for robotics to work, we have to somehow unlock the power of scaling law. But where does that come from?" "It's a profound problem that everybody's battling with in robotics." "We see a lot of unlock in being able to do this whole process through the modeling of the environments." "Being able to create these digital worlds... that's just going to unlock so much more potential for being able to replace all the costly and the unsafe data in the real environments with the data generated from the worlds for robots to be able to do scalable learning and evaluations." Fei-Fei Li Yunzhu Li martin_casado

World Labs CEO Dr. Fei-Fei Li & SceniX Co-Founder Yunzhu Li on the data bottleneck in robotics and how world models help: "The lack of data in training, the lack of data in evaluation, this is very, very different from language models, where data is abundant on the internet." "And we know that in order for robotics to work, we have to somehow unlock the power of scaling law. But where does that come from?" "It's a profound problem that everybody's battling with in robotics." "We see a lot of unlock in being able to do this whole process through the modeling of the environments." "Being able to create these digital worlds... that's just going to unlock so much more potential for being able to replace all the costly and the unsafe data in the real environments with the data generated from the worlds for robots to be able to do scalable learning and evaluations." Fei-Fei Li Yunzhu Li martin_casado

a16z

76,473 次观看 • 1 个月前

🚨 Jensen Huang says everyone panicked about the AI data when MOST training data was never REAL to begin with. Ilya Sutskever told the industry pre-training was over. "Ilya said, 'We're out of data,' or something like that. 'Pre-training is over,' or something like that," Huang says. "The industry panicked, you know, that this is the end of AI." "And of course, of course that's obviously not true. We're gonna keep on scaling the amount of data that we have to train with." "A lot of that data is probably gonna be synthetic." That's where the panic came from — synthetic data sounds like cheating. "Most of the data that we are training, that we teach each other with, inform each other with, is synthetic." "It's synthetic because it didn't come out of nature." "You created it. I'm consuming it. I modify it, augment it, I regenerate it, somebody else consumes it." The textbook in your hand is synthetic. The post you're reading is synthetic. The lecture you took is synthetic. Nature didn't make any of it. Humans did. AI just learned to do the same thing — faster. "Training is now limited by compute," Huang says. "Data is now limited by compute." The data wall wasn't a wall. It was a mirror. If you're new here, follow @AiEvolutio for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast

🚨 Jensen Huang says everyone panicked about the AI data when MOST training data was never REAL to begin with. Ilya Sutskever told the industry pre-training was over. "Ilya said, 'We're out of data,' or something like that. 'Pre-training is over,' or something like that," Huang says. "The industry panicked, you know, that this is the end of AI." "And of course, of course that's obviously not true. We're gonna keep on scaling the amount of data that we have to train with." "A lot of that data is probably gonna be synthetic." That's where the panic came from — synthetic data sounds like cheating. "Most of the data that we are training, that we teach each other with, inform each other with, is synthetic." "It's synthetic because it didn't come out of nature." "You created it. I'm consuming it. I modify it, augment it, I regenerate it, somebody else consumes it." The textbook in your hand is synthetic. The post you're reading is synthetic. The lecture you took is synthetic. Nature didn't make any of it. Humans did. AI just learned to do the same thing — faster. "Training is now limited by compute," Huang says. "Data is now limited by compute." The data wall wasn't a wall. It was a mirror. If you're new here, follow @AiEvolutio for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast

AI Evolution

15,565 次观看 • 3 个月前

Been looking into RICE AI ( ◉ - ◉ ) lately, and it’s one of the few projects actually tackling a real challenge in robotics and AGI: the lack of high-quality training data. Robots can’t get truly intelligent without it. They building a decentralized way to collect and share robotics data, using a DePIN network to train physical AI foundation models. The project was even hand-picked by CZ as a winner of the Dubai BNB Chain Demo Day, and it’s backed by Floki while also being part of the NVIDIA Inception Program. They also launched FLOKI Minibot M1 - a small AI companion that doubles as a data node. Every interaction with it feeds the RICE AI Foundry, the first decentralized platform for Robotics Foundation Models, and contributors earn $RICE tokens in return. And it’s working: – 800 minibots sold in 24h – 33,000% oversubscribed Binance IDO – 500+ robots already deployed worldwide Feels like a project with real long-term potential. Worth keeping an eye on. RICE AI ( ◉ - ◉ )

Been looking into RICE AI ( ◉ - ◉ ) lately, and it’s one of the few projects actually tackling a real challenge in robotics and AGI: the lack of high-quality training data. Robots can’t get truly intelligent without it. They building a decentralized way to collect and share robotics data, using a DePIN network to train physical AI foundation models. The project was even hand-picked by CZ as a winner of the Dubai BNB Chain Demo Day, and it’s backed by Floki while also being part of the NVIDIA Inception Program. They also launched FLOKI Minibot M1 - a small AI companion that doubles as a data node. Every interaction with it feeds the RICE AI Foundry, the first decentralized platform for Robotics Foundation Models, and contributors earn $RICE tokens in return. And it’s working: – 800 minibots sold in 24h – 33,000% oversubscribed Binance IDO – 500+ robots already deployed worldwide Feels like a project with real long-term potential. Worth keeping an eye on. RICE AI ( ◉ - ◉ )

Christiaan

12,696 次观看 • 1 年前

Leju Robotics has launched a massive 10,000-square-meter data training center in Beijing, aimed at solving the industry’s data shortage and bridging the "reality gap" for AI models. The facility features 1:1 replicas of 16 real-world scenarios across industrial, home, and healthcare sectors. ► Scale: Over 10,000 sq. meters, featuring 16 detailed real-world scenarios. ► Capacity: Annual production capacity of over 6 million high-quality data entries. ► Training: The Kuavo humanoid robot is already training on-site, achieving a 95%+ success rate on practical tasks like sorting, packing, and inspection. ► Quality: Data sourced from real machine runs, certified at a 99% single-data qualification rate.

Leju Robotics has launched a massive 10,000-square-meter data training center in Beijing, aimed at solving the industry’s data shortage and bridging the "reality gap" for AI models. The facility features 1:1 replicas of 16 real-world scenarios across industrial, home, and healthcare sectors. ► Scale: Over 10,000 sq. meters, featuring 16 detailed real-world scenarios. ► Capacity: Annual production capacity of over 6 million high-quality data entries. ► Training: The Kuavo humanoid robot is already training on-site, achieving a 95%+ success rate on practical tasks like sorting, packing, and inspection. ► Quality: Data sourced from real machine runs, certified at a 99% single-data qualification rate.

RoboHub🤖

43,066 次观看 • 11 个月前

Is $4/hr to complete various tasks okay for you?? 🚨 Do not miss this latest update! $4/hr to Upload videos of you cleaning or doing irl tasks. (no face) Get paid for training AI 4$/hr (Payment to your Bank Account) ✔️Sign up with "Google" or Email ✔️Link: ✔️Register by filling your details ✔️Complete verification (use nin plastic card for fast verification) ✔️Choose from varieties of tasks to earn ✔️You also get $5 when your invite completes a task ✔️Payment is instant Watch Video guide….⚠️ to get started! Join the AlphaLynx Channel for more updates and support.! Link 🔗 in comments.

Is $4/hr to complete various tasks okay for you?? 🚨 Do not miss this latest update! $4/hr to Upload videos of you cleaning or doing irl tasks. (no face) Get paid for training AI 4$/hr (Payment to your Bank Account) ✔️Sign up with "Google" or Email ✔️Link: ✔️Register by filling your details ✔️Complete verification (use nin plastic card for fast verification) ✔️Choose from varieties of tasks to earn ✔️You also get $5 when your invite completes a task ✔️Payment is instant Watch Video guide….⚠️ to get started! Join the AlphaLynx Channel for more updates and support.! Link 🔗 in comments.

MacKhaddy🩺

31,308 次观看 • 2 个月前

Announcing Mantis UMI, a pair of handheld data collection grippers for teams working on physical AI. Collect accurate bimanual manipulation data in the real world 10× faster than teleoperation. Mantis uses the exact same grippers as Axol, so you can feel what Axol feels. Data collected with Mantis transfers directly to Axol. Mantis runs on Open Source software and is modular so you can chose the tracker that works best you: Vive Tracker, Quest Controllers, and more. Collect it. Train it. Run it on Axol. Available Today 👇

Announcing Mantis UMI, a pair of handheld data collection grippers for teams working on physical AI. Collect accurate bimanual manipulation data in the real world 10× faster than teleoperation. Mantis uses the exact same grippers as Axol, so you can feel what Axol feels. Data collected with Mantis transfers directly to Axol. Mantis runs on Open Source software and is modular so you can chose the tracker that works best you: Vive Tracker, Quest Controllers, and more. Collect it. Train it. Run it on Axol. Available Today 👇

Almond Robotics

30,368 次观看 • 25 天前

Tired of teleoperation? One human video → 1,000s of robot demos. (📍GitHub ) Scaling Robot Data Without Dynamics Simulation or Robot Hardware Real2Render2Real (R2R2R) is a new way to scale robot data without physics simulation or hardware. You take a phone scan + a single monocular human demo. It tracks the motion, renders photorealistic scenes, and generates diverse, robot-agnostic trajectories ready for training. > No teleop, no sim, no robot, just a phone and a video > Train VLA models and diffusion policies directly on the output > Supports multiple robot embodiments with kinematic consistency > 1000s of demos in 1/27 the time of real-world collection Thank you, Max Fu, for sharing!! Project: Paper: Code coming soon: It shows that with the right pipeline, you can scale robot learning data without touching a robot. One of the most interesting directions in scalable robotics today. —— Weekly robotics and AI insights. Subscribe free:

Tired of teleoperation? One human video → 1,000s of robot demos. (📍GitHub ) Scaling Robot Data Without Dynamics Simulation or Robot Hardware Real2Render2Real (R2R2R) is a new way to scale robot data without physics simulation or hardware. You take a phone scan + a single monocular human demo. It tracks the motion, renders photorealistic scenes, and generates diverse, robot-agnostic trajectories ready for training. > No teleop, no sim, no robot, just a phone and a video > Train VLA models and diffusion policies directly on the output > Supports multiple robot embodiments with kinematic consistency > 1000s of demos in 1/27 the time of real-world collection Thank you, Max Fu, for sharing!! Project: Paper: Code coming soon: It shows that with the right pipeline, you can scale robot learning data without touching a robot. One of the most interesting directions in scalable robotics today. —— Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

42,864 次观看 • 7 个月前