AdaDemo: 面向通用机器人智能体的数据高效示教扩展
AdaDemo: Data-Efficient Demonstration Expansion for Generalist Robotic Agent
AI总结：
为解决模仿学习中示教数据数量与质量限制通用机器人智能体学习的问题，提出AdaDemo自适应在线示教扩展框架，通过主动收集针对性示教数据提升多任务策略，在22项机器人操作任务上验证了其数据高效的性能提升效果。
摘要：
受语言和视觉基础模型显著成就的鼓舞，通过使用大型示教数据集进行模仿学习来开发通用机器人智能体，已成为机器人学习领域一个备受关注的方向。模仿学习的效果在很大程度上依赖于示教数据集的数量和质量。在本研究中，我们旨在以数据高效的方式扩大示教数据规模，以促进通用机器人智能体的学习。我们提出了AdaDemo（自适应在线示教扩展），这是一个通用框架，旨在通过主动且持续地扩展示教数据集来改进多任务策略学习。AdaDemo会战略性地收集新的示教数据，以解决现有策略中已识别的薄弱点，确保最大化数据效率。通过在两个机器人操作基准（RLBench和Adroit）上共计22项任务的综合评估，我们证明了AdaDemo能够以数据高效的方式引导生成高质量示教数据集，从而逐步提升策略性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
