Control barrier functions (CBFs) enable guaranteed safe multi-agent
navigation in the continuous domain. The resulting navigation performance,
however, is highly sensitive to the underlying hyperparameters. Traditional
approaches consider fixed CBFs (where parameters are tuned apriori), and hence,
typically do not perform well in cluttered and highly dynamic environments:
conservative parameter values can lead to inefficient agent trajectories, or
even failure to reach goal positions, whereas aggressive parameter values can
lead to infeasible controls. To overcome these issues, in this paper, we
propose online CBFs, whereby hyperparameters are tuned in real-time, as a
function of what agents perceive in their immediate neighborhood. Since the
explicit relationship between CBFs and navigation performance is hard to model,
we leverage reinforcement learning to learn CBF-tuning policies in a model-free
manner. Because we parameterize the policies with graph neural networks (GNNs),
we are able to synthesize decentralized agent controllers that adjust parameter
values locally, varying the degree of conservative and aggressive behaviors
across agents. Simulations as well as real-world experiments show that (i)
online CBFs are capable of solving navigation scenarios that are infeasible for
fixed CBFs, and (ii), that they improve navigation performance by adapting to
other agents and changes in the environment