Solving Multi-Entity Robotic Problems Using Permutation Invariant Neural Networks
<|reference_start|>Solving Multi-Entity Robotic Problems Using Permutation Invariant Neural Networks: 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.<|reference_end|>
@article{an2024solving,
    title={Solving Multi-Entity Robotic Problems Using Permutation Invariant Neural
  Networks},
    author={Tianxu An, Joonho Lee, Marko Bjelonic, Flavio De Vincenti, Marco
  Hutter},
    journal={arXiv preprint arXiv:2402.18345},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.18345},
    primaryClass={cs.RO}
}