  Challenges in real-world robotic applications often stem from managing
multiple, dynamically varying entities such as neighboring robots, manipulable
objects, and navigation goals. Existing multi-agent control strategies face
scalability limitations, struggling to handle arbitrary numbers of entities.
Additionally, they often rely on engineered heuristics for assigning entities
among agents. We propose a data driven approach to address these limitations by
introducing a decentralized control system using neural network policies
trained in simulation. Leveraging permutation invariant neural network
architectures and model-free reinforcement learning, our approach allows
control agents to autonomously determine the relative importance of different
entities without being biased by ordering or limited by a fixed capacity. We
validate our approach through both simulations and real-world experiments
involving multiple wheeled-legged quadrupedal robots, demonstrating their
collaborative control capabilities. We prove the effectiveness of our
architectural choice through experiments with three exemplary multi-entity
problems. Our analysis underscores the pivotal role of the end-to-end trained
permutation invariant encoders in achieving scalability and improving the task
performance in multi-object manipulation or multi-goal navigation problems. The
adaptability of our policy is further evidenced by its ability to manage
varying numbers of entities in a zero-shot manner, showcasing near-optimal
autonomous task distribution and collision avoidance behaviors.
