## 1 Introduction  
Solving mobile robot navigation has been at the forefront of scientific studies for decades [1, 2]. Over the course of extensive development, we have seen many approaches that try to encode reason into a robotic body so as to enable it to react to its surroundings in a reasonable and intelligent manner. However, the idea that a human-derived, rule-based navigation system is sufficient for safe and generalizable motion has not come to fruition. It has become more and more evident that, while specified rules for robot behavior work well in limited and controlled environments, it is incredibly difficult to deploy them in unseen or dynamic scenes in which other agents are not controllable or fully observable. In such cases, the complexity of the necessary rule-based system becomes unattainable. For more than a decade, researchers have looked towards deep learning (DL) as an intelligent and encompassing solution for mobile robot navigation. DL has been applied to robots' learning of local motion policies based on sensor input [3, 4]. This has allowed robots to learn optimized motion strategies in various settings based solely on sensor data and minimal hand engineering, and it opens up the possibility of obtaining navigation strategies that take into account not only observations of the ego-robot itself, but also reasoning about other participants in the shared environment. This is highly applicable in the floor-cleaning robot domain, in which the cleaning robots share spaces with workers, customers, and other equipment. Here, the safety of other agents and equipment paramount, as is the robot's ability to interact with and make navigation decisions, considering them in a reasonable manner. A cleaning robot is required to carry out its task while considering these other environmental actors, all while being constrained by the inability to freely maneuver and the absence of backward motion due to its cleaning brush design. Therefore, reacting and interacting with other agents is even more important. While human actors generally move slowly and can easily move out of the way of a robot, it is other machinery that needs to be considered more stringently. In such locations as logistics centers and factories, cleaning robots are often deployed alongside loaders and other similar machinery. This machinery has its own set of motion restrictions, and motion disagreements between it and a cleaning robot can be very costly. Therefore, the motion of this kind of machinery should be taken into account when developing navigation policies for such mobile robots as automated floor cleaners. Owing to the fact that other machinery can also include localization and communication systems, its spatial information can be obtained directly. If no communication is possible, certain motion features can be inferred from observations. While exact methods of information exchange remain an open question and are highly dependent on the capabilities of the robots at hand, their sensors, communication devices, and surrounding environment, such technical possibilities cannot be ignored, and methods of using this information should be implemented. This allows for the possibility of training such motion policies that not only consider the ego robot, but also other navigation agents in the environment. However, to be able to generalize the interaction with other agents, the exchanged information needs to be translation and rotation invariant and to allow for a varying number of scene actors. This reduces the distribution shift for trainable policies and generalizes the approach [5]. Therefore, by expanding on our previous work in training floor cleaning robot motion policies [6], we propose including a graph-like structured interaction edge embedder into our trained neural network architecture in order to extract ego-robot frame-aligned interaction features. This allows the robot to learn its motion policy not only conditioned on its direct observations of the environment but also on the motion and temporal information of other agents.