Task Demonstration

Task demonstration focuses on efficiently acquiring high-quality data for training robots and AI agents, primarily through human-provided examples. Current research emphasizes improving data collection methods, such as using augmented reality interfaces to reduce demonstrator workload and developing algorithms that leverage large language models (LLMs) to synthesize or interpret demonstrations, often incorporating techniques like in-context learning and imitation learning. These advancements aim to reduce the cost and effort of data acquisition, enabling more robust and generalizable AI systems for various applications, including robotic manipulation and computer control.