Genie Studio is a one-stop development platform created by AgiBot, specifically designed for embodied intelligence scenarios.
Genie Studio covers the entire embodied closed-loop process, including data collection, dataset management, model training and fine-tuning, simulation evaluation, model construction, and deployment, to accelerate implementation and application of embodied intelligence technologies.
Provide a data collection solution that covers the entire data lifecycle, efficiently collecting massive amounts of data with a single machine capable of producing up to 1,000 episodes of data per day.
Provide proprietary and open-source robot base models, bridging the entire pipeline from training, fine-tuning, quantization to deployment, thereby reducing the barriers to training.
Provide simulation and evaluation capabilities, with over 6,000 object assets and simulation scenarios, enabling user-side scenario reconstruction, collection of expert trajectory data, and visualization of evaluation results.
Provide a "one-click real-machine deployment" capability, migrating algorithms from the cloud to real machine environments, with a 2-3 times improvement in single-GPU inference performance compared to traditional solutions.
Only supports native robots, lacks multi-end effector data collection capability
Task customization is inflexible and lacks intelligent features; no real-time viewing during collection
No real-time data validation on edge devices; asynchronous upload unavailable
Cloud data processing is inefficient and opaque
URDF driver compatibility issues across various robotic embodiments and end effectors
Missing unified data alignment review for auto pre-labeling, manual annotation, atomic skills, joint visualization, and URDF data
High cost of real world data, and inefficient generalization of training dataset
Enables rapid integration of diverse robot configurations into the data acquisition platform
Facilitates data collection task creation/distribution, real-time viewing, data validation, and asynchronous upload
Template-based configuration with efficient task management, supporting flexible scenario-object variable combinations
Cloud-native architecture with auto-scaling capabilities and customizable data validation mechanisms
Custom URDF drivers for heterogeneous robotic embodiments and end effectors
Cloud-based auto pre-labeling with unified alignment of atomic skills, operation videos, joint data, and URDF through centralized manual audit
Synthetic data scenario generation, trajectory augumentation and automated collection to expand model training data
High barriers of model training - Excessive costs in computing power, data acquisition, and model architecture design
Inefficient video data loading - Heavy network/storage consumption during transmission and frame decoding
Disjointed edge-cloud resource management - Lack of systematic scheduling across heterogeneous nodes
Absence of standardized training frameworks - Difficulties in troubleshooting and performance optimization
Overly complex R&D pipelines - Incomplete traceability mechanisms across full workflows
Intelligent Computing Engine: Integrates modules for data, algorithms, evaluation, and deployment, supporting multi-cloud deployment for rapid implementation
Hardware-Accelerated Video Data Loading: Enables random frame access through hardware-accelerated video decoding, minimizing network and storage resource consumption
Edge-Cloud Cluster Management: Unified orchestration of cloud and edge node clusters, enabling collaborative scheduling of heterogeneous resources across clusters
Proprietary Training Framework: Engineered foundational framework for model training, decoupling algorithm development from framework iteration
End-to-End Pipeline Orchestration: Unified resource scheduling across edge and cloud environments, establishing a fully observable delivery pipeline with comprehensive workflow monitoring
Low fidelity between simulated and real-world environments: Poor modeling accuracy and distorted physical parameters lead to algorithm migration failures
High data collection costs for complex tasks: Long-duration operations rely on manual teaching, resulting in inefficiency.
Insufficient diversity in simulation data: Monotonous scenarios and lack of perturbations degrade algorithm generalization capabilities.
Low confidence in simulation evaluation: Significant discrepancies between simulation results and physical device testing render simulations inadequate for real-world validation.
Inefficient large-scale testing: Slow single-machine simulation speed fails to support concurrent simulation testing demands.
High-fidelity simulation asset library: 1:1 reconstruction of real-world scenes and objects via 3D scanning, with physics engine parameters aligned with AgiBot World's real robot data.
Intelligent data generalization framework: Combine teleoperation data collection with automatic trajectory augmentation, generating thousands of enhanced datasets per operation.
Full-domain randomization engine: Automated perturbations of lighting/material/dynamic parameters enable million-scale differentiated scenario generation.
Sim2Real close-loop validation: Achieves less than 5% sim to real error on GO-1 model, providing 10+ industrial-grade evaluation criteria including collision detection, task completion rate, and trajectory accuracy.
Cloud-based simulation platform: Enables multi-concurrent automated testing, boosting task throughput by 100x, with real-time data visualization and statistical reporting.
Challenging application deployment
Complex heterogeneous hardware adaptation
Tedious algorithm integration
Lack of application management
One-click deployment for robotic applications
Supports model optimization, encryption, multi-platform publishing, and hardware heterogeneity
Provides GDK integration interfaces to rapidly empower robotic agents with "intelligence"
All-in-one application management platform: Feature-rich, simple, and user-friendly
