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AGIBOT introduces AgiBot World, a large-scale open-source dataset for general-purpose robotic learning. ⦿ 1M+ trajectories from 100+ real-world scenarios ⦿ Scenarios include dexterous manipulation, tool use, and multi-robot collaboration

The Humanoid Hub

66,297 subscribers

59,847 просмотров • 1 год назад •via X (Twitter)

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Комментарии: 6

Фото профиля The Humanoid Hub

The Humanoid Hub1 год назад

The dataset spans five application domains and offers more long examples than other open datasets. Available for download on Hugging Face:

Фото профиля The Humanoid Hub

The Humanoid Hub1 год назад

AGIBOT stands out for its rapid scaling among Chinese startups.

Фото профиля Yang

Yang1 год назад

Want to learn how practical AI skills and automations for your business and work? Check out our 50+ step-by-step video tutorials 100% FREE 20+ hours of Ai and Automation goodness absolutely free 🥳

Фото профиля Tom Löwe

Tom Löwe1 год назад

Just give it legs, my god. 🦿

Фото профиля Let's learnrobot.com®

Let's learnrobot.com®1 год назад

🤖👋

Фото профиля Appy Pie

Appy Pie1 год назад

AgiBot World is a game-changer for robotic learning! 1M+ trajectories across real-world scenarios will drive major innovations.

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28,035 просмотров • 1 год назад

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🚀 Unitree open-sources UnifoLM-WBT-Dataset — a high-quality real-world humanoid robot whole-body teleoperation (WBT) dataset for open environments. 🥳Publicly available since March 5, 2026, the dataset will continue to receive high-frequency rolling updates. It aims to establish the most comprehensive real-world humanoid robot dataset in terms of scenario coverage, task complexity, and manipulation diversity. 👉 Explore the dataset here:

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Unitree

5,629,954 просмотров • 2 месяцев назад

Humanoid Robots on a Real Production Line: AGIBOT Livestream From June 23–28, AGIBOT will host a livestream from a tablet mass-production line, showcasing humanoid robots operating across a full quality-inspection section in a real factory environment alongside human operators. Over six days, viewers will get a live look at real production workflows, human-robot collaboration, multi-robot coordination, and long-duration operations under actual industrial conditions. As humanoid robotics moves from demonstrations to real-world deployment, the livestream offers a rare opportunity to see how humanoid robots perform on an active production line. 📅 June 23–28, 2026 📍 Longcheer Technology, Nanchang Factory ▶ Watch live on AGIBOT Official X（ Youtube ( Channel.

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AGIBOT

24,211 просмотров • 25 дней назад

A big part of scaling robot learning to solve real-world problems is that we somehow need to get enough diverse, high-quality data to train our robots to perform useful things. GPT and its fellow large language models were bootstrapped and proved out on a massive dataset of real-world language data. Unfortunately, despite our best efforts, similarly massive datasets don’t really exist for robotics — so, in our unending pursuit of high-quality, useful data, we turn to simulation. I compared a couple recent works on sim-to-real robot manipulation, which discuss how to train perception-driven manipulation policies in simulation, in such a way that they’re useful in the real world. - DextraH-RGB, from NVIDIA - Sim-and-Real Co-Training: A Simple Recipe for Vision-Based Robotic Manipulation, also from NVIDIA — specifically the GEAR lab - Sim-to-Real Reinforcement Learning for Vision-Based Dexterous Manipulation on Humanoids, another GEAR lab paper - Local Policies Enable Zero-shot Long-Horizon Manipulation, from CMU (video from DextrAH-RGB)

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Exciting progress on Vision-Language-Action models from a collaboration between San Francisco-based Physical Intelligence (π) and China’s AGIBOT: (π)’s single model can autonomously perform diverse tasks on the AGIBOT G1 robot, using both humanoid hands and two-finger grippers.

The Humanoid Hub

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TidyBot: Personalized Robot Assistance with Large Language Models approach enables fast adaptation and achieves 91.2% accuracy on unseen objects in our benchmark dataset. We also demonstrate our approach on a real-world mobile manipulator called TidyBot, which successfully puts away 85.0% of objects in real-world test scenarios abs: project page: paper page:

TidyBot: Personalized Robot Assistance with Large Language Models approach enables fast adaptation and achieves 91.2% accuracy on unseen objects in our benchmark dataset. We also demonstrate our approach on a real-world mobile manipulator called TidyBot, which successfully puts away 85.0% of objects in real-world test scenarios abs: project page: paper page:

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Between factories and homes, Shanghai-based AGIBOT Robotics is deploying the AGIBOT A2 in large quantities in commercial service scenarios, such as shopping malls, showrooms, and auto dealerships. They mass-produced 1,000 units earlier this year and plan to produce 5,000 this year. The A2 is a full-sized humanoid robot powered by local AI and equipped with emotional interaction capabilities to provide user-friendly interactive services.

CyberRobo

100,999 просмотров • 10 месяцев назад

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

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Text-to-image generative models, meet robotics! We present ROSIE: Scaling RObot Learning with Semantically Imagined Experience, where we augment real robotics data with semantically imagined scenarios for downstream manipulation learning. Website: 🧵👇

Text-to-image generative models, meet robotics! We present ROSIE: Scaling RObot Learning with Semantically Imagined Experience, where we augment real robotics data with semantically imagined scenarios for downstream manipulation learning. Website: 🧵👇

Fei Xia

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Introducing RL-100: Performant Robotic Manipulation with Real-World Reinforcement Learning. 7 real robot tasks, 900/900 successes. Up to 250 consecutive trials in one task, running 2 hours nonstop without failure. High success rate against physical disturbances, zero-shot, and few-shot adaptation Our first step toward a deployable robot learning system.

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Our Robotic Foundation Model, trained on data from real-world warehouse operations and simulated scenarios, enables our robots to identify objects, understand 3D spaces, and predict optimal grasping and placement.

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