  Encouraged by the remarkable achievements of language and vision foundation
models, developing generalist robotic agents through imitation learning, using
large demonstration datasets, has become a prominent area of interest in robot
learning. The efficacy of imitation learning is heavily reliant on the quantity
and quality of the demonstration datasets. In this study, we aim to scale up
demonstrations in a data-efficient way to facilitate the learning of generalist
robotic agents. We introduce AdaDemo (Adaptive Online Demonstration Expansion),
a general framework designed to improve multi-task policy learning by actively
and continually expanding the demonstration dataset. AdaDemo strategically
collects new demonstrations to address the identified weakness in the existing
policy, ensuring data efficiency is maximized. Through a comprehensive
evaluation on a total of 22 tasks across two robotic manipulation benchmarks
(RLBench and Adroit), we demonstrate AdaDemo's capability to progressively
improve policy performance by guiding the generation of high-quality
demonstration datasets in a data-efficient manner.
