Learning local behavioral sequences to better infer non-local properties in real multi-robot systems

When members of a multi-robot team follow regular motion rules sensitive to robots and other environmental factors within sensing range, the team itself may become an informational fabric for gaining situational awareness without explicit signalling among robots. In our previous work [1], we used machine learning to develop a scalable module, trained only on data from 3-robot teams, that could predict the positions of all robots in larger multi-robot teams based only on observations of the movement of a robot's nearest neighbor. Not only was this approach scalable from 3-to-many robots, but it did not require knowledge of the control laws of the robots under observation, as would a traditional observer-based approach. However, performance was only tested in simulation and could only be a substitute for explicit communication for short periods of time or in cases of very low sensing noise. In this work, we apply more sophisticated machine learning methods to data from a physically realized robotic team to develop Remote Teammate Localization (ReTLo) modules that can be used in realistic environments. To be specific, we adopt Long-Short-Term-Memory (LSTM) [2] to learn the evolution of behaviors in a modular team, which has the effect of greatly reducing errors from regression outcomes. In contrast with our previous work in simulation, all of the experiments conducted in this work were conducted on the Thymio physical, two-wheeled robotic platform.