Visual Template Management for Efficient Collection

Integrating project management, task allocation, progress tracking, and status analysis into a unified platform. With templated configurations, data collection tasks can be generated with one click. Real-time tracking of the entire workflow ensures smoother collaboration and more efficient data acquisition.

Flexible Configurations with Diverse Platforms & End Effectors

The system supports data collection across various robot platforms and end-effector configurations. Its robust data standardization capability enables end-to-end processing from diverse devices to high-quality model training data.

High-Concurrency Architecture for Scalable Operations

Equipped with the technical capability to support hundreds of robots performing synchronous data collection. Through high-concurrency architecture and load-balanced scheduling, the platform ensures real-time reception and processing of massive data streams.

24/7 Online Collection, Stable and Reliable

Built on a highly available service architecture, the platform enables reliable 24/7 data collection. With extensive format compatibility, collected data can be directly output or converted into mainstream training formats, accelerating R&D efficiency.

Rich Ecosystem with Mainstream Model Support

An open model ecosystem is established, featuring built-in community datasets and support for open-source dataset training. It offers deep integration with leading open-source models such as PI and GROOT.

Simulation Environment for Model Evaluation

Includes a high-fidelity, high-precision 3D asset library. By constructing realistic simulation environments, it rapidly generates comprehensive evaluation schemes to support algorithm validation.

User-Friendly Design for Rapid Deployment

Fully deployable out-of-the-box, minimizing setup time.minimizing setup time. Launch model development instantly via "one-click training," leverage integrated simulation tools for reliable evaluation, and achieve seamless migration from algorithms to real-world machines.

Distributed Training for High Efficiency

Built on a high-performance distributed training architecture, the platform enables elastic scheduling of computing tasks and parallel acceleration. It dynamically scales based on workload resources, achieving up to 90% GPU utilization.