China AI helps humanoid robots handle more objects with less training

Researchers at Wuhan University in China have developed a novel AI framework called RGMP (recurrent geometric-prior multimodal policy) to enhance humanoid robot manipulation capabilities. RGMP integrates geometric reasoning with efficient learning to improve grasping accuracy and enable robots to handle a wider variety of objects and more complex tasks with significantly less training data. Unlike many existing data-driven methods that require large datasets and struggle to generalize beyond familiar environments, RGMP achieves 87 percent generalization accuracy and is five times more data-efficient than leading diffusion-based models. The framework consists of two main components: the Geometric-prior Skill Selector (GSS), which chooses appropriate actions based on object shape and task needs using geometric rules, and the Adaptive Recursive Gaussian Network (ARGN), which models spatial memory over time to improve learning from limited examples. The team tested RGMP on both humanoid and desktop dual-arm robots using a dataset of 120 demonstration trajectories, comparing its performance against state-of-the-art models like ResNet50