This paper presents a three-phase computational methodology for making informed design decisions when determining the allocation of work and the interaction modes for human-robot teams. The methodology highlights the necessity to consider constraints and dependencies in the work and the work environment as a basis for team design, particularly those dependencies that arise within the dynamics of the team’s collective activities. These constraints and dependencies form natural clusters in the team’s work, which drive the team’s performance and behavior. The proposed methodology employs network visualization and computational simulation of work models to identify dependencies resulting from the interplay of taskwork distributed between teammates, teamwork, and the work environment. Results from these analyses provide insight into not only team efficiency and performance, but also quantified measures of required teamwork, communication, and physical interaction. The paper describes each phase of the methodology in detail and demonstrates each phase with a case study examining the allocation of work in a human-robot team for space operations.

This document describes a demonstration of the computational simulation framework Work Models that Compute (WMC). WMC is a framework for the objective evaluation of function allocation between humans and robots. The WMC framework has been used to study the impact of function allocation on both the air traffic management and spacecraft operations work domains. Recent advances include modeling of human-robot control modes, locomotion, failures and physical resources. We propose a demonstration of WMC showcasing how WMC can provide useful, objective and quantitative insight in the trade-offs asso-ciated with function allocation. In the demonstration, we will analyze function allocation for an on-orbit maintenance scenario. We will first show how minor changes to a function allocations can have major ef-fects on the emergent work patterns that result from the simulation framework. Second, we will demon-strate an analysis of 10-15 possible function allocations (defined in advance) and cross-compare their char-acteristics based on measures such as the idle time, taskload for each agent, information transfer require-ments, physical resources exchanges and others. This will show the framework’s capability to rapidly ex-plore the function allocation trade-space and allow the designer to make more informed trade-offs.

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