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. Our
code and videos are available at
this https URL.

本文提出一种基于循环图神经网络和注意力机制的新型 RL 方法，通过捕捉时空中多种交互类型来预测人类行为意图，并将该预测应用于无模型 RL 框架中以避免机器人干扰其他个体，从而在人群中实现机器人的安全、高效导航。

基于关注交互图的意图感知机器人人群导航

Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph

For robots to be a part of our daily life, they need to be able to navigate
among crowds not only safely but also in a socially compliant fashion. This is
a challenging problem because humans tend to navigate by implicitly cooperating
with one another to avoid collisions, while heading toward their respective
destinations. Previous approaches have used hand-crafted functions based on
proximity to model human-human and human-robot interactions. However, these
approaches can only model simple interactions and fail to generalize for
complex crowded settings. In this paper, we develop an approach that models the
joint distribution over future trajectories of all interacting agents in the
crowd, through a local interaction model that we train using real human
trajectory data. The interaction model infers the velocity of each agent based
on the spatial orientation of other agents in his vicinity. During prediction,
our approach infers the goal of the agent from its past trajectory and uses the
learned model to predict its future trajectory. We demonstrate the performance
of our method against a state-of-the-art approach on a public dataset and show
that our model outperforms when predicting future trajectories for longer
horizons.

本文提出了一种模型，通过模拟人 - 人和人 - 机之间的交互，在考虑空间定位的前提下预测机器人在人群中的未来轨迹，对比实验表明该模型对于长时间段内的轨迹预测表现优于现有技术。