Quality Over Quantity: Curating Contact-Based Robot Datasets Improves Learning

TL;DR

This paper demonstrates that selecting highly informative contact-based data using a contact-aware metric enhances robot learning efficiency, emphasizing quality over quantity in dataset curation.

Contribution

The authors introduce a contact-aware Fisher-information metric to rank and curate contact data, improving learning outcomes and reducing data requirements in contact-based robot learning.

Findings

Curated contact data accelerates learning.

Less but informative data can outperform larger datasets.

The metric guides effective data curation for robot learning.

Abstract

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.