Given a swarm of limited-capability robots, we seek to automatically discover the set of possible emergent behaviors. Prior approaches to behavior discovery rely on human feedback or hand-crafted behavior metrics to represent and evolve behaviors and only discover behaviors in simulation, without testing or considering the deployment of these new behaviors on real robot swarms. In this work, we present Real2Sim2Real Behavior Discovery via Self-Supervised Representation Learning, which combines representation learning and novelty search to discover possible emergent behaviors automatically in simulation and enable direct controller transfer to real robots. First, we evaluate our method in simulation and show that our proposed self-supervised representation learning approach outperforms previous hand-crafted metrics by more accurately representing the space of possible emergent behaviors. Then, we address the reality gap by incorporating recent work in sim2real transfer for swarms into our lightweight simulator design, enabling direct robot deployment of all behaviors discovered in simulation on an open-source and low-cost robot platform.

翻译：给定一个能力有限的机器人集群，我们致力于自动发现所有可能的涌现行为。现有的行为发现方法依赖于人工反馈或人工设计的行为度量来表征与演化行为，且仅在仿真中发现行为，并未测试或考虑将这些新行为部署到真实的机器人集群上。在本工作中，我们提出了基于自监督表征学习的Real2Sim2Real行为发现方法，该方法结合表征学习与新奇性搜索，在仿真中自动发现可能的涌现行为，并实现控制器向真实机器人的直接迁移。首先，我们在仿真中评估了所提方法，结果表明我们提出的自监督表征学习方法通过更准确地表征可能涌现行为空间，优于先前基于人工设计度量的方法。随后，我们通过将近期集群仿真到真实迁移的研究成果融入轻量化仿真器设计，解决了现实差距问题，使得所有在仿真中发现的行为能够直接部署于一个开源且低成本的机器人平台上。