How Users Understand Robot Foundation Model Performance through Task Success Rates and Beyond

TL;DR

This paper investigates how non-expert users interpret robot foundation model performance data, emphasizing the importance of task success rates and additional information for understanding robot capabilities and risks.

Contribution

It provides empirical insights into how non-experts interpret RFM evaluation data, highlighting the value of success rates and failure case information for informed decision-making.

Findings

Non-experts interpret success rates similarly to experts.

Failure case descriptions are highly valued by users.

Users desire access to both real evaluation data and predictive estimates.

Abstract

Robot Foundation Models (RFMs) represent a promising approach to developing general-purpose home robots. Given the broad capabilities of RFMs, users will inevitably ask an RFM-based robot to perform tasks that the RFM was not trained or evaluated on. In these cases, it is crucial that users understand the risks associated with attempting novel tasks due to the relatively high cost of failure. Furthermore, an informed user who understands an RFM's capabilities will know what situations and tasks the robot can handle. In this paper, we study how non-roboticists interpret performance information from RFM evaluations. These evaluations typically report task success rate (TSR) as the primary performance metric. While TSR is intuitive to experts, it is necessary to validate whether novices also use this information as intended. Toward this end, we conducted a study in which users saw real evaluation data, including TSR, failure case descriptions, and videos from multiple published RFM research projects. The results highlight that non-experts not only use TSR in a manner consistent with expert expectations but also highly value other information types, such as failure cases that are not often reported in RFM evaluations. Furthermore, we find that users want access to both real data from previous evaluations of the RFM and estimates from the robot about how well it will do on a novel task.