Core Insights - The article discusses the advancements in the RLinf framework, particularly the release of RLinf v0.2, which supports real-world reinforcement learning and aims to enhance the capabilities of embodied intelligence systems [3][5]. Group 1: RLinf v0.2 Features - RLinf v0.2 allows users to utilize robots as flexible resources similar to GPUs, enabling the deployment of workers on robots by simply accessing their IP and port [3][6]. - The framework supports heterogeneous soft and hardware cluster configurations, accommodating the diverse requirements of real-world reinforcement learning [8][10]. - RLinf v0.2 introduces a fully asynchronous off-policy algorithm design, which decouples inference and training nodes, significantly improving training efficiency [11][14]. Group 2: Experimental Results - The initial version of RLinf v0.2 was tested using a Franka robotic arm on two tasks: Charger and Peg Insertion, achieving convergence within 1.5 hours for both tasks [12][15]. - The success rates for the tasks were impressive, with Peg Insertion achieving over 100 consecutive successes and Charger over 50 consecutive successes after training [15][18]. - The training process was documented through videos, showcasing the simultaneous operation of two Franka robotic arms in different locations [16][23]. Group 3: Development Philosophy - The RLinf team emphasizes the collaborative evolution of algorithms and infrastructure, aiming to create a new research ecosystem for embodied intelligence [20]. - The team is composed of members from various institutions, including Tsinghua University and Peking University, highlighting a diverse background in infrastructure, algorithms, and robotics [20].