Launch of Robo-ValueRL: The First Open-Source VLA Reinforcement Learning Framework for Robotics

The Beijing Humanoid Robot Innovation Center and Renmin University of China's Gaoling Artificial Intelligence Institute have launched the Robo-ValueRL open-source framework. This initiative aims to enhance humanoid robots' decision-making capabilities in precision tasks, such as semiconductor assembly, by addressing challenges in data quality, control precision, and adaptability in dynamic environments.

Robo-ValueRL introduces a value estimation mechanism based on historical observations, enabling robots to autonomously assess their actions. This closed-loop learning process—observation, value estimation, correction, and iteration—allows for improved accuracy and reduced instability in operations. The framework is fully open-source, providing access to core algorithms, evaluation tools, and standardized protocols for universities, research institutions, and manufacturers.

The open-source nature of Robo-ValueRL significantly lowers the barriers for small and medium-sized manufacturers to implement reinforcement learning in specialized fields like semiconductor production and medical device manufacturing. This development marks a shift in humanoid robotics from laboratory experiments to practical industrial applications, paving the way for robots to evolve their decision-making capabilities independently.