As robotics and AI research grows more complex, companies are seeking simulation platforms that bridge digital and real-world tasks. Today, AGIBOT Innovation Technology Co. announced the release of Genie Sim 3.0 at CES 2026, aiming to address this demand. Genie Sim 3.0, built on NVIDIA Isaac Sim, merges digital asset creation, data collection, scene generation, and automated benchmarks through a single software pipeline. This launch signals AGIBOT’s intention to support faster and more standardized embodied intelligence development worldwide. Developers and researchers are increasingly looking for open platforms where they can test advanced robotic behaviors with minimal hardware investment.
Several previous simulation tools have offered modular environments, but they often lacked the unified evaluation methodology or breadth of scenarios now found in Genie Sim 3.0. In earlier announcements, AGIBOT focused on hardware releases like the humanoid AGIBOT A2, but this new push emphasizes software and data accessibility. Earlier platforms also tended to limit scenario creation to manual input or constrained templates, while Genie Sim 3.0 leverages LLMs for natural scene description and extensive scenario variety, a step further than what most competitors delivered before. Compared to prior synthetic datasets from AGIBOT or peers, the current offering provides greater task variety, more automation in data annotation, and integration of industrial-scene datasets, supporting more robust benchmark creation.
What Features Define Genie Sim 3.0’s Platform?
Genie Sim 3.0 centers on a unified workflow, bringing together simulation asset creation, digital scene construction, high-fidelity physics, and automated evaluation tools. It processes over 10,000 hours of synthetic data and integrates advanced 3D reconstruction via Skyland Innovation’s MetaCam handheld laser scanners, translating real-world footage into simulation environments in under a minute. AGIBOT utilizes large language models (LLMs) for intuitive scene generation, meaning users can input natural language scene descriptions and quickly receive usable, structured virtual tasks for robotic training. The system’s vision-language models help fine-tune generated scenes to fit diverse specifications or research needs.
How Does Genie Sim 3.0 Support Benchmarking and Evaluation?
The platform incorporates Genie Sim Benchmark, a suite for standardized evaluation across over 200 tasks spanning more than 100,000 scenarios. This evaluation module automatically generates workflows based on scene and manipulation demands, constructing broad capability profiles for embodied AI models and highlighting both strengths and improvement areas. In the words of AGIBOT’s team,
“Our evaluation system is designed to give a comprehensive picture of a model’s abilities, not just a single score.”
Open-sourcing all simulation assets and benchmarks allows widespread collaboration and consistency in measuring robotic intelligence.
What Makes AGIBOT’s Data Resources Unique?
Besides offering a large suite of scenarios and evaluation tasks, AGIBOT provides over 10,000 hours of open-source synthetic data incorporating various sensor modalities, such as RGB-D, stereo vision, and full-body kinematic tracking. The company’s intelligent data-collection toolkit can resume failed tasks, enabling efficient data gathering for both teleoperation and automation. Automated annotation further accelerates dataset building. AGIBOT emphasizes community access by allowing public downloads through GitHub. According to the company,
“Providing open-source tools and datasets is fundamental for accelerating community-driven robotics research.”
The approach aims to minimize time and financial commitment for researchers and industry users alike.
AGIBOT’s launch of Genie Sim 3.0 highlights a shift toward open, modular simulation platforms that balance ease-of-use and comprehensive analytics for embodied intelligence. By leveraging recent advances in scene generation using large language models and rapid 3D asset creation, the platform addresses key development bottlenecks. The combination of open-source policy, automated evaluation, and support for diverse robot types positions Genie Sim 3.0 as a significant tool for both early-stage and advanced robotics research. Users aiming to reduce dependency on hardware can train and validate algorithms based on standardized, widely recognized benchmarks, supporting fair comparisons across the industry. As robotics moves forward, simulation-driven workflows will likely become essential for developing robust, generalizable AI agents that operate in dynamic real-world environments. For teams or individuals beginning in robotics research, open access to data and evaluation resources can accelerate learning and innovation, while providing transparency and repeatability in model assessment. AGIBOT’s focus on unified evaluation and open tools may well influence broader practices throughout the field.
