机器人操作中从大规模数据集学习的关键要素
What Matters in Learning from Large-Scale Datasets for Robot Manipulation
AI总结：
本研究针对机器人操作大规模数据集收集与利用的认知空白，开发可控数据生成框架开展数据集构成研究，明确了相机位姿等关键多样性维度，提出的检索策略在真实场景中最高提升70%性能。
摘要：
从大型多任务演示数据集进行模仿学习已成为构建通用能力机器人的一条极具前景的路径。因此，全球各地已投入数千小时来构建此类大规模数据集。尽管这类工作的投入持续增长，我们仍缺乏系统性的认知，即应当收集何种数据才能提升机器人数据集的实用性，并促进下游策略学习。在本研究中，我们开展了一项大规模数据集构成研究来解答这一问题。我们开发了一个数据生成框架，以程序化方式模拟现有数据集中常见的多样性来源（例如传感器布局、物体类型与摆放方式），并利用该框架生成构成可控的大规模机器人数据集，从而支持一系列在现实世界中成本过高而无法开展的数据集构成研究。我们聚焦于两个实际场景：（1）未来研究者收集机器人领域大规模数据集时，应当重点关注哪些类型的多样性；（2）当前从业者应当如何从现有数据集中检索相关演示，以最大化下游策略在目标任务上的性能。本研究得出了若干关键洞见——例如，我们发现相机位姿和空间布局是收集中的多样性和检索中的对齐都涉及的关键维度。在真实世界机器人学习场景中，我们发现不仅我们从仿真中得到的洞见具备可迁移性，而且我们在DROID等现有数据集上采用的检索策略，能够持续优于现有训练策略，性能提升幅度最高达70%。更多结果详见https://robo-mimiclabs.github.io/
英文摘要：
Imitation learning from large multi-task demonstration datasets has emerged as a promising path for building generally-capable robots. As a result, 1000s of hours have been spent on building such large-scale datasets around the globe. Despite the continuous growth of such efforts, we still lack a systematic understanding of what data should be collected to improve the utility of a robotics dataset and facilitate downstream policy learning. In this work, we conduct a large-scale dataset composition study to answer this question. We develop a data generation framework to procedurally emulate common sources of diversity in existing datasets (such as sensor placements and object types and arrangements), and use it to generate large-scale robot datasets with controlled compositions, enabling a suite of dataset composition studies that would be prohibitively expensive in the real world. We focus on two practical settings: (1) what types of diversity should be emphasized when future researchers collect large-scale datasets for robotics, and (2) how should current practitioners retrieve relevant demonstrations from existing datasets to maximize downstream policy performance on tasks of interest. Our study yields several critical insights -- for example, we find that camera poses and spatial arrangements are crucial dimensions for both diversity in collection and alignment in retrieval. In real-world robot learning settings, we find that not only do our insights from simulation carry over, but our retrieval strategies on existing datasets such as DROID allow us to consistently outperform existing training strategies by up to 70%. More results at https://robo-mimiclabs.github.io/
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。
