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Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph
We study the problem of safe and intention-aware robot navigation in dense
and interactive crowds. Most previous reinforcement learning (RL) based methods
fail to consider different types of interactions among all agents or ignore the
intentions of people, which results in performance degradation. In this paper,
we propose a novel recurrent graph neural network with attention mechanisms to
capture heterogeneous interactions among agents through space and time. To
encourage longsighted robot behaviors, we infer the intentions of dynamic
agents by predicting their future trajectories for several timesteps. The
predictions are incorporated into a model-free RL framework to prevent the
robot from intruding into the intended paths of other agents. We demonstrate
that our method enables the robot to achieve good navigation performance and
non-invasiveness in challenging crowd navigation scenarios. We successfully
transfer the policy learned in simulation to a real-world TurtleBot 2i
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