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Genie Studio
Overview :
Genie Studio is a one-stop development platform specifically designed by Zhiyuan Robotics for embodied AI scenarios, with full-chain product capabilities covering data acquisition, model training, simulation evaluation, and model inference. It provides developers with a standardized solution from 'acquisition' to 'training' to 'testing' to 'inference', greatly reducing the development threshold and improving development efficiency. The platform promotes the rapid development and application of embodied AI technology through efficient data acquisition, flexible model training, precise simulation evaluation, and seamless model inference. Genie Studio not only provides powerful tools but also supports the large-scale implementation of embodied AI, accelerating the industry's leap to a new stage of standardization, platformization, and mass production.
Target Users :
Genie Studio is suitable for robot developers, research institutions, universities, and enterprises engaged in embodied AI research and applications. It provides these users with a one-stop development platform to efficiently conduct data acquisition, model training, simulation evaluation, and model inference, thereby accelerating the R&D and application of embodied AI technology. Through Genie Studio, users can break free from data silos and computing power constraints, allowing robots to continuously evolve in a blended virtual and real environment, driving the transformation of embodied AI from a code executor to an environment perceiver.
Use Cases
Robot developers utilize Genie Studio to quickly develop and deploy embodied AI algorithms, improving the robots' operational capabilities in complex environments
Research institutions use Genie Studio for embodied AI research, accelerating the transition from the laboratory to practical applications
Universities utilize Genie Studio to conduct embodied AI teaching and research projects, cultivating professionals in this field
Features
Provides data solutions covering the entire data lifecycle, efficiently collecting massive amounts of data with a single-machine daily capacity of up to 1000 items, providing high-quality data support for model training
Provides self-developed and mainstream open-source robot base models, connecting the training, fine-tuning, quantization, and deployment links, lowering the training threshold, and supporting collaborative training of open-source/private datasets
Provides simulation evaluation capabilities, with over 6000 object assets and simulation scenarios, enabling user-side scene reconstruction, expert trajectory data acquisition, and visualization of evaluation results, helping to quickly verify algorithm performance and optimize models
Provides 'one-click real-machine deployment' capability, easily achieving seamless migration of algorithms from the cloud to the real-machine environment, with single-card inference performance improved by 2-3 times compared to traditional solutions, accelerating the large-scale implementation of embodied AI
Supports multi-body and multi-end device management, providing full-process functional services, including batch template-based data acquisition task generation, full-chain annotation and visual inspection, and dataset management
Equipped with a distributed architecture data pipeline, efficiently completing data cleaning, anomaly detection, and other processing, supporting both real-time and offline processing modes to ensure that massive data is 'used immediately after acquisition'
Features VR and keyboard teleoperation functions, supporting rapid verification of real-machine teleoperation functions in simulation, precise evaluation results, and a GO-1 model simulation test result error of less than 5% compared to real-machine results
Integrates key capabilities such as a model compiler, GDK real-machine control system, low-code development system, application development framework, and application publishing system, building an 'algorithm-deployment-management' closed loop
How to Use
Visit the Genie Studio website to learn about platform functions and usage guides
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Register and log in to the platform, create a project, and select the appropriate robot model and dataset
Use the data acquisition module to collect or import multimodal data according to project needs
Utilize the model training module to select pre-trained models or custom models for training and fine-tuning
In the simulation evaluation module, build simulation scenarios and conduct algorithm performance evaluation and optimization
Through the model inference module, deploy the trained model to a real machine environment for actual testing with one click
Based on the evaluation results and actual test feedback, further optimize the model and algorithm to achieve iterative improvement and application of embodied AI
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