Machine learning (ML) - based techniques enable robots to perform a range of tasks in complex environments. Research in this area has increased significantly in recent years, with various ML methods being explored to allow robots to make decisions based on task requirements and changes in their surroundings. However, some of the tested approaches, such as reinforcement and imitation learning, often struggle with training stability and capturing the multimodal nature of behavior. In this blog post, Tzu-Yuan Huang introduces a promising ML approach for robotic decision-making: diffusion models.
👉 If you want to learn more about it, check it out: https://zuseschoolrelai.de/blog/robotic-decision-making-diffusion-models/
