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Multi-robot task allocation is a ubiquitous problem in robotics due to itsapplicability in a variety of scenarios. Adaptive task-allocation algorithmsaccount for unknown disturbances and unpredicted phenomena in the environmentwhere robots are deployed to execute tasks. However, this adaptivity typicallycomes at the cost of requiring precise knowledge of robot models in order toevaluate the allocation effectiveness and to adjust the task assignment online.As such, environmental disturbances can significantly degrade the accuracy ofthe models which in turn negatively affects the quality of the task allocation.In this paper, we leverage Gaussian processes, differential inclusions, androbust control barrier functions to learn environmental disturbances in orderto guarantee robust task execution. We show the implementation and theeffectiveness of the proposed framework on a real multi-robot system.

Data-Driven Adaptive Task Allocation for Heterogeneous Multi-Robot Teams Using Robust Control Barrier Functions