Human-robot teams for disaster response need to dynamically adapt their task allocation and coordination to the momentary context, based on adequate trust stances and taking account of the relevant values and norms (such as safety, health, and privacy). This paper presents a Work Agreement framework that supports this capability. The research question is: Which minimal set of concepts, relations, and associated formalization can be used to model Work Agreements with adequate expressiveness and flexibility? An ontology has been developed that defines core concepts with their relations (creditor, debtor, antecedent, consequent, lifespan, acceptance), encompassing the knowledge to specify, activate, monitor, and reason about work agreements. The framework was implemented and tested as part of the TRADR project. The TRADR system brought forward the desired adaptive team behavior of the concerning robot. The tests led to further refinements of the work agreements framework.

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We report Year 4 progress in the TRADR project WP5: Persistent models for human-robot teaming. We focused on the analysis, modelling and online-processing of the information-gathering tasks that the human-robot team is performing during a mission, with the goal to enable the robotic system to follow the mission (understand which tasks have been assigned to whom, what the progress is) and provide support for the management of the activities through the agent system and based on the working agreements. The reported work includes further development of team communication processing, ontology modelling, task management support, working agreements. The developed modules are integrated in the TRADR system and were evaluated during the TRADR evaluation exercise.@en

We report on a field exercise in which a team of human fire-fighters used robots to enact a realistic disaster response mission in an industrial environment. In this exercise we evaluated the technical working of an integrated robotic system and gained insights concerning the manner in which robots and information streams can be utilized effectively. We have learnt important lessons regarding the employment of human-robot teams in complex, realistic missions.@en