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Systems that unify data gathering from both physical robotic hardware and simulation environments into a single format.
Distinguishing note: None of the candidates cover the unification of real-world and simulated data into a single replay buffer.
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RLinf is a distributed reinforcement learning orchestrator and embodied AI training framework. It provides the infrastructure to train vision-language-action models and robotic policies using a combination of reinforcement learning and supervised fine-tuning. The system is designed for scaling workloads across GPU clusters, managing the placement of actors, rollout workers, and environment components. It features a specialized robotics data collection pipeline for gathering teleoperated demonstrations and simulation trajectories into standardized replay buffers, alongside a hardware interface
Gathers and stores interaction data from both real-world robots and simulators into a standardized replay buffer.