Scientific embodied training platform
Layered modeling · Physical & functional fidelity · Science-oriented simulation · Scalable laboratory learning
Layered & Progressive Modeling
Builds digital twins progressively from single devices to workstations and full laboratories. Ensures consistent fidelity, modular scalability, and seamless integration across scales.
Physical & Functional Fidelity
Captures both physical parameters (geometry, material, inertia) and operational actions such as grasping, rotating, and pipetting — bridging the sim-to-real gap for dexterous manipulation.
Science-Oriented Simulation
Goes beyond motion simulation by embedding the scientific meaning of devices and experimental actions, enabling authentic scientific reasoning and protocol-level training.
Scalable Training Scenarios
Expands from unit workstations to laboratory clusters, supporting multi-robot collaboration, logistics coordination, and long-horizon autonomous experiment campaigns under realistic constraints.
Equipment Digital Twins
Creates high-fidelity 3D models as the foundation for spatial collision detection, kinematic interaction, and realistic sensor simulation in LabGym — capturing both geometry and behavioral response.
Functional Action Digital Twins
Models kinematic chains and operational logic to synchronize control commands with virtual actions, enabling realistic robotic skill execution and reproducible experimental protocols.
Scientific Semantic Annotation
Builds rich datasets linking physical parameters, functional actions, and scientific meanings (e.g., "heating to 60°C" correlates with protein denaturation). Enables systematic simulation and knowledge transfer.
Laboratory-Level Collaborative Simulation
Simulates multi-device workflows, resource scheduling, logistics linkage, spatial coordination, and global optimization — training embodied agents for real lab operations.