  This paper presents a data-driven decentralized trajectory optimization
approach for multi-robot motion planning in dynamic environments. When
navigating in a shared space, each robot needs accurate motion predictions of
neighboring robots to achieve predictive collision avoidance. These motion
predictions can be obtained among robots by sharing their future planned
trajectories with each other via communication. However, such communication may
not be available nor reliable in practice. In this paper, we introduce a novel
trajectory prediction model based on recurrent neural networks (RNN) that can
learn multi-robot motion behaviors from demonstrated trajectories generated
using a centralized sequential planner. The learned model can run efficiently
online for each robot and provide interaction-aware trajectory predictions of
its neighbors based on observations of their history states. We then
incorporate the trajectory prediction model into a decentralized model
predictive control (MPC) framework for multi-robot collision avoidance.
Simulation results show that our decentralized approach can achieve a
comparable level of performance to a centralized planner while being
communication-free and scalable to a large number of robots. We also validate
our approach with a team of quadrotors in real-world experiments.
