正在加载视频...

视频加载失败

🚀 AGIBOT Genie Sim 3.0: First LLM-Driven Open-Source Simulation Platform for Embodied AI @ CES 2026 Integrated with NVIDIA Isaac Sim, AGIBOT’s all-new Genie Sim 3.0 ( debuts at CES - delivering a unified toolchain for digital asset generation, scene generalization, data collection, and automated evaluation. Key highlights: ✅...

19,578 次观看 • 9 个月前 •via X (Twitter)

4 条评论

The AI Index 的头像
The AI Index9 个月前

Open source plus sim to real sounds great, curious how much actually transfers without hand tuning

Himanshu Kumar 的头像
Himanshu Kumar9 个月前

@AGIBOTofficial, LLM-driven simulation opens new avenues for embodied AI, especially with NVIDIA Isaac Sim integration.

Angel 的头像
Angel9 个月前

thank you!

Nikhil Deshpande 的头像
Nikhil Deshpande9 个月前

Simulation is becoming the control plane for embodied AI. LLM-driven, open simulation stacks are how robots learn faster, safer, and at scale — long before touching the real world.

相关视频

🔥 JUST IN: Open-source robotics dataset from 100% real-world scenarios! 🤯 Chinese robotics company AGIBOT just released AGIBOT WORLD 2026, an open-source dataset systematically covering key embodied AI research directions. Built entirely from real-world environments: commercial spaces, and homes. Collected using AGIBOT G2 robots in free-form collection mode, providing structured, accurately annotated, high-quality data. Digital twin technology creates 1:1 scale replicas in simulation matching the real environments. Both real-world and simulation data are open-sourced. The AGIBOT G2 platform collects multiple data types simultaneously: RGB(D) cameras, tactile sensors, force sensors, LiDAR, IMU, and full-body joint states. Whole-body control coordinates arms, waist, and hands for complex tasks. First-person teleoperation lets operators control the robot from its perspective. The tasks covered are fine-grained manipulation, ultra-long-horizon tasks, spatial navigation, dual-arm coordination, and multi-agent/human-robot collaboration. The dataset includes error-recovery trajectories with annotations. Most datasets only show successful demonstrations. AGIBOT includes failures and how the robot recovers, teaching models how to handle mistakes. After collection, data is tested through policy training and real-robot deployment to ensure quality. Then processed through industrial quality control with multiple screening and cleaning rounds. Making it open-source accelerates embodied AI research by giving researchers access to high-quality real-world robot data at scale. 🇨🇳 Learn more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

40,583 次观看 • 6 个月前