Research on Robotics Diffusion Transformer in Robot Foundation Models Based on Diffusion Model

In recent years, diffusion models have shown great potential in the field of robotics. This paper focuses on the research of robot foundation models based on diffusion models, with a particular emphasis on the Robotics Diffusion Transformer (RDT). First, it reviews the technological frontier of robot basic action models, including visual language models and robot action learning. Then, it details the RDT model, covering its design for an ALOHA dual - arm robot, core methods such as diffusion model design, feature coding, regularization, non - linear action output, and Alternating Condition Injection (ACI). The RDT model is trained on a large number of datasets and fine - tuned on specific data. Experimental results demonstrate that RDT outperforms previous models in tasks such as generalization to unseen objects and scenes, few - shot learning, and dexterous control. Ablation experiments further verify the effectiveness of its key design elements. Overall, RDT proves the significance of generative models in embodied intelligence, showing that appropriate model design and data - driven training can achieve excellent results.