Mobility in an effective and socially-compliant manner is an essential yet challenging task for robots operating in crowded spaces. Recent works have shown the power of deep reinforcement learning techniques to learn socially cooperative policies. However, their cooperation ability deteriorates as the crowd grows since they typically relax the problem as a one-way Human-Robot interaction problem. In this work, we want to go beyond first-order Human-Robot interaction and more explicitly model Crowd-Robot Interaction (CRI). We propose to (i) rethink pairwise interactions with a self-attention mechanism, and (ii) jointly model Human-Robot as well as Human-Human interactions in the deep reinforcement learning framework. Our model captures the Human-Human interactions occurring in dense crowds that indirectly affects the robot's anticipation capability. Our proposed attentive pooling mechanism learns the collective importance of neighboring humans with respect to their future states. Various experiments demonstrate that our model can anticipate human dynamics and navigate in crowds with time efficiency, outperforming state-of-the-art methods.
翻译:以有效和符合社会要求的方式流动对于在拥挤的空间运作的机器人来说是一项重要而又具有挑战性的任务。最近的工作显示,深入强化学习技术的力量足以学习社会合作政策。然而,随着人群的增多,他们的合作能力会恶化,因为他们通常会放松单向人类-机器人互动问题。在这项工作中,我们希望超越人类-机器人互动的第一阶和更明确的模范Crowd-Robot互动(CRI),我们提议(一)重新思考与自我注意机制的对称互动,(二)在深度强化学习框架中,人类-机器人以及人类-人类互动的联合模型。我们的模型捕捉到在密集人群中发生的人类-人类互动,间接影响机器人的预期能力。我们提议的“关注集合机制”了解了相邻人类对其未来状态的集体重要性。各种实验都表明,我们的模型能够以时间效率、优异的状态方法预测人类动态和在人群中航行。