Training Pipeline
We Train Robots, Not Just Models. Our pipeline bridges the gap between digital simulation and physical reality through a rigorous, multi-layered training system.
Robot Architectures
We design physical intelligence systems that bridge the gap between simulation and reality, focusing on the unique requirements of each task environment.
Embodiment
The physical form of a robot is as critical as its software. We engineer bodies that are optimized for specific environments, from warehouse logistics to automotive assembly.
Sensors
Our robots are equipped with advanced perception systems that process visual, tactile, and auditory data in real-time to navigate complex physical environments.
Workflows
We map the entire physical workflow, from initial human demonstration to final deployment, ensuring seamless integration into existing enterprise operations.
AI Logic & Frameworks
Decoding the architecture of intelligence: from autonomous driving parallels to the evolution of physical AI systems.
Autonomous Driving Parallels
We map the logical progression of autonomous vehicles—perception, understanding, prediction, and control—to physical AI systems, ensuring seamless transition from digital to physical environments.
End-to-End vs. VLA
Exploring the trade-offs between holistic physical AI and Vision-Language-Action frameworks. We build customized data and policies to optimize learning for specific robotic embodiments.
Simulation & Validation
From NVIDIA Isaac Sim to OpenVLA, we leverage high-fidelity simulation and human demonstration to train robots in safe, controlled environments before real-world deployment.
Physical AI Ecosystem
Our framework ecosystem integrates ROS 2 and open-source learning into a cohesive pipeline, from workflow decomposition to continuous learning and enterprise co-development.
Development Pipeline
We Train Robots, Not Just Models: From digital twin to sim-to-real deployment.
01
Use Case & Embodiment
We define the physical task and robot embodiment, mapping the environment and action space for precise simulation.
02
Digital Twin Creation
We build a high-fidelity digital twin of the physical environment, ensuring every sensor and mechanical detail is replicated.
03
Physics Simulation
We utilize NVIDIA Isaac Sim and Isaac Lab to run physics-based simulations, allowing for safe and scalable policy training.
04
Human Demonstration
We integrate human demonstration and imitation learning to transfer complex physical skills and task understanding into code.
05
Lab Validation
We validate the trained policies in a physical lab environment, ensuring robustness and safety before enterprise deployment.
06
Sim-to-Real Deployment
We deploy the trained intelligence into the physical world, bridging the gap between simulation and real-world performance.
Training Labs & Scenarios
We engineer high-fidelity environments for the next generation of physical intelligence, bridging the gap between simulation and the real world.
