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.

本研究通过 AdaDemo（自适应在线演示扩展）框架，在机器人学习中引入了新的演示数据集扩充方法，以提高多任务策略学习的效果，通过对 22 个任务的全面评估，展示了 AdaDemo 在改进策略性能、引导高质量演示数据集生成方面的能力，实现了数据的有效利用。