  In this paper we consider the problem of navigation and motion control in an
area densely populated with other agents. We propose an algorithm that, without
explicit communication and based on the information it has, computes the best
control action for all the agents and implements its own. Notably, the host
agent (the agent executing the algorithm) computes the differences between the
other agents' computed and observed control actions and treats them as known
disturbances that are fed back into a robust control barrier function (RCBF)
based quadratic program. A feedback loop is created because the computed
control action for another agent depends on the previously used disturbance
estimate. In the case of two interacting agents, stability of the feedback loop
is proven and a performance guarantee in terms of constraint adherence is
established. This holds whether the other agent executes the same algorithm or
not.
