  There are two major challenges for scaling up robot navigation around dynamic
obstacles: the complex interaction dynamics of the obstacles can be hard to
model analytically, and the complexity of planning and control grows
exponentially in the number of obstacles. Data-driven and learning-based
methods are thus particularly valuable in this context. However, data-driven
methods are sensitive to distribution drift, making it hard to train and
generalize learned models across different obstacle densities. We propose a
novel method for compositional learning of Sequential Neural Control Barrier
models (SNCBFs) to achieve scalability. Our approach exploits an important
observation: the spatial interaction patterns of multiple dynamic obstacles can
be decomposed and predicted through temporal sequences of states for each
obstacle. Through decomposition, we can generalize control policies trained
only with a small number of obstacles, to environments where the obstacle
density can be 100x higher. We demonstrate the benefits of the proposed methods
in improving dynamic collision avoidance in comparison with existing methods
including potential fields, end-to-end reinforcement learning, and
model-predictive control. We also perform hardware experiments and show the
practical effectiveness of the approach in the supplementary video.
