  High-speed, low-latency obstacle avoidance that is insensitive to sensor
noise is essential for enabling multiple decentralized robots to function
reliably in cluttered and dynamic environments. While other distributed
multi-agent collision avoidance systems exist, these systems require online
geometric optimization where tedious parameter tuning and perfect sensing are
necessary.
  We present a novel end-to-end framework to generate reactive collision
avoidance policy for efficient distributed multi-agent navigation. Our method
formulates an agent's navigation strategy as a deep neural network mapping from
the observed noisy sensor measurements to the agent's steering commands in
terms of movement velocity. We train the network on a large number of frames of
collision avoidance data collected by repeatedly running a multi-agent
simulator with different parameter settings. We validate the learned deep
neural network policy in a set of simulated and real scenarios with noisy
measurements and demonstrate that our method is able to generate a robust
navigation strategy that is insensitive to imperfect sensing and works reliably
in all situations. We also show that our method can be well generalized to
scenarios that do not appear in our training data, including scenes with static
obstacles and agents with different sizes. Videos are available at
https://sites.google.com/view/deepmaca.
