This project is a comprehensive research platform designed for the end-to-end lifecycle of robotic learning. It provides a modular framework for training neural network policies—specifically through imitation and reinforcement learning—and deploying them onto physical robotic hardware. By offering a unified interface for hardware abstraction, the platform decouples high-level control logic from the specific sensors and actuators of diverse robotic systems.
The framework distinguishes itself through a standardized approach to data and policy management. It utilizes a consistent schema for recording and sharing interaction data, which includes synchronized video and state information. To support complex training requirements, it features distributed optimization across multiple graphics processing units and a kinematic engine that handles coordinate transformations between joint space and Cartesian systems. These capabilities are complemented by a flexible architecture that allows for the modular design of vision-language-action models.
Beyond core training, the platform includes extensive utilities for data processing, such as observation standardization and action normalization, ensuring compatibility across different environments and hardware configurations. It also provides integrated tools for benchmarking performance through standardized rollout loops and evaluation scripts. For resource-constrained hardware, the system supports remote inference streaming, allowing computational workloads to be offloaded to external servers while maintaining real-time control.
