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Robots operating in real world settings must navigate and maintain safetywhile interacting with many heterogeneous agents and obstacles. Multi-AgentControl Barrier Functions (CBF) have emerged as a computationally efficienttool to guarantee safety in multi-agent environments, but they assume perfectknowledge of both the robot dynamics and other agents' dynamics. Whileknowledge of the robot's dynamics might be reasonably well known, theheterogeneity of agents in real-world environments means there will always beconsiderable uncertainty in our prediction of other agents' dynamics. This workaims to learn high-confidence bounds for these dynamic uncertainties usingMatrix-Variate Gaussian Process models, and incorporates them into a robustmulti-agent CBF framework. We transform the resulting min-max robust CBF into aquadratic program, which can be efficiently solved in real time. We verify viasimulation results that the nominal multi-agent CBF is often violated duringagent interactions, whereas our robust formulation maintains safety with a muchhigher probability and adapts to learned uncertainties

Safe Multi-Agent Interaction through Robust Control Barrier Functions with Learned Uncertainties