AdaDemo: Data-Efficient Demonstration Expansion for Generalist Robotic Agent

Tongzhou Mu, Yijie Guo, Jie Xu, Ankit Goyal, Hao Su, Dieter Fox, Animesh Garg

Keywords: Demonstration Expansion, Multi-task Policy Learning, Robotics

TL;DR: We propose AdaDemo, which aims to improve multi-task policy learning by strategically expanding the demonstration dataset.

Abstract: 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.

Submission Number: 36