Researchers from Stanford University have proposed a low-cost holistic teleoperation system named Mobile ALOHA for collecting data on holistic teleoperation. Mobile ALOHA extends the capabilities of the original ALOHA by placing it on a wheeled base, giving it mobility. The researchers utilized a static ALOHA dataset for imitation learning, achieving good performance in mobile operation tasks through pre-training and joint training. This study addresses tasks that require holistic teleoperation on a daily basis.

DeepMind has launched a new artificial intelligence agent system that learns tasks directly from human teachers. The system learns through imitation learning in a cultural transmission manner, without relying on human data. The research published in the journal Nature details the process and advantages of imitation learning. This approach has significant implications for training robots and customer service systems. DeepMind addresses data collection and privacy issues in AI training.

Research shows that the MimicGen system can generate large-scale training data for robots. By leveraging digital twin technology, it replicates human operation data in a simulated environment. Using fewer than 200 human demonstrations, it autonomously generates 50,000 training data points. Synthetic data and simulation are crucial for AI development, providing an infinite source of training data. MimicGen is suitable for long-sequence tasks and high-precision tasks, such as grasping and assembling.