The ultimate navigation efficiency of mobile robots in human environments will depend on how we will appraise them: merely as impersonal machines or as human-like agents. In the latter case, an agent may take advantage of the cooperative collision avoidance, given that it possesses recursive cognition, i.e.,the agent's decisions depend on the decisions made by humans that in turn depend on the agent's decisions. To deal with this high-level cognitive skill, we propose a neural network architecture implementing Prediction-for-CompAction paradigm. The network predicts possible human-agent collisions and compacts the time dimension by projecting a given dynamic situation into a static map. Thereby emerging compact cognitive map can be readily used as a "dynamic GPS" for planning actions or mental evaluation of the convenience of cooperation in a given context. We provide numerical evidence that cooperation yields additional room for more efficient navigation in cluttered pedestrian flows, and the agent can choose path to the target significantly shorter than a robot treated by humans as a functional machine. Moreover, the navigation safety, i.e., the chances to avoid accidental collisions, increases under cooperation. Remarkably, these benefits yield no additional load to the mean society effort. Thus, the proposed strategy is socially compliant, and the humanoid agent can behave as "one of us".

翻译：移动机器人在人类环境中的最终导航效率将取决于我们如何评价它们：仅仅作为非人格化的机器，还是作为类人智能体。在后一种情况下，若智能体具备递归认知能力——即智能体的决策依赖于人类的决策，而人类的决策反过来又依赖于智能体的决策——它便可以利用协作避碰策略。为应对这一高级认知技能，我们提出了一种实现"预测-紧致行动"范式的神经网络架构。该网络预测可能的人机碰撞，并通过将给定动态情境投影到静态地图上来压缩时间维度。由此产生的紧致认知地图可便捷地用作"动态GPS"，用于规划行动或对特定情境下协作便利性进行心理评估。我们提供的数值证据表明：协作能为在拥挤人流中实现更高效导航创造额外空间，且智能体可选择比被人类视作功能性机器的机器人显著更短的路径到达目标。此外，导航安全性（即避免意外碰撞的概率）在协作条件下得以提升。值得注意的是，这些益处并未给社会平均努力带来额外负担。因此，所提策略具有社会合规性，类人智能体能够表现得"像我们一样"。