Social robots have evolved in diverse applications with the emergence of deep reinforcement learning methods. However, safe and secure navigation of social robots in a complex crowded environment remains a challenging task. The robot can safely navigate in a crowd only if it can predict the next action of humans, however this task becomes difficult because of the unpredictable human behavior. To address the issue of socially compliant navigation, the robot needs to learn real-time human behavior. This manuscript models Danger-Zone for the robot by considering all possible actions that humans can take at given time. The Danger Zones are formulated by considering the real time human behavior. The robot is trained to avoid these danger zones for safe and secure navigation. The proposed model is tested on the three state of art methods, Collision Avoidance with Deep Reinforcement Learning (CADRL), Long Short Term Memory Reinforcement Learning (LSTM-RL) and Social Attention with Reinforcement Learning (SARL) in multi-agent navigation. Experimental results signify that proposed model can understand human behavior and navigate in a socially compliant manner with safety as the highest priority.

中文翻译：

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利用深度强化学习，通过人类行为相似性在拥挤环境中进行符合社交要求的机器人导航


随着深度强化学习方法的出现，社交机器人在不同的应用中不断发展。然而，社交机器人在复杂拥挤的环境中安全可靠的导航仍然是一项具有挑战性的任务。只有能够预测人类的下一步行动，机器人才能在人群中安全地导航，然而，由于人类行为的不可预测性，这项任务变得很困难。为了解决符合社会规范的导航问题，机器人需要学习实时人类行为。该手稿通过考虑人类在给定时间可以采取的所有可能行动来为机器人建立危险区域模型。危险区域是通过考虑实时人类行为来制定的。机器人经过训练可以避开这些危险区域，以实现安全可靠的导航。所提出的模型在三种最先进的方法上进行了测试：多智能体导航中的深度强化学习碰撞避免（CADRL）、长短期记忆强化学习（LSTM-RL）和强化学习社交注意力（SARL）。实验结果表明，所提出的模型可以理解人类行为，并以符合社会规范的方式进行导航，并以安全为最高优先级。