用接触数据质量取代数量，提升机器人学习效率
Quality Over Quantity: Curating Contact-Based Robot Datasets Improves Learning
- 基于接触信息构建评估指标，筛选高价值数据
- 少而精的数据集使学习更快更稳定，优于海量数据
- 适合需要高效数据采集的机器人操控研究
本文研究数据集对机器人学习的影响，探讨更多数据与高质量数据孰优孰劣。重点关注接触数据的价值——接触蕴含丰富机器人学习所需信息。提出一种接触感知的目标函数，用于从位姿和接触数据中学习物体动力学与形状。发现接触感知的Fisher信息度量可有效评估数据的表征能力，并据此排序与筛选数据。实验表明，基于该度量选取的精简数据集不仅提升学习性能，还使学习过程趋于确定性。有趣的是，数据量并非越多越好；在依赖接触交互的任务中，少量高信息量数据反而加速学习。最后，该度量可为接触式机器人学习提供初始数据筛选指导。
原文摘要 · Abstract (English)
In this paper, we investigate the utility of datasets and whether more data or the 'right' data is advantageous for robot learning. In particular, we are interested on quantifying the utility of contact-based data as contact holds significant information for robot learning. Our approach derives a contact-aware objective function for learning object dynamics and shape from pose and contact data. We show that the contact-aware Fisher-information metric can be used to rank and curate contact-data based on how informative data is for learning. In addition, we find that selecting a reduced dataset based on this ranking improves the learning task while also making learning a deterministic process. Interestingly, our results show that more data is not necessarily advantageous, and rather, less but informative data can accelerate learning, especially depending on the contact interactions. Last, we show how our metric can be used to provide initial guidance on data curation for contact-based robot learning.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。