用自监督学习自动发现机器人集群新行为并直接部署到真实机器人。
Discovery and Deployment of Emergent Robot Swarm Behaviors via Representation Learning and Real2Sim2Real Transfer
- 通过自监督表征学习自动捕捉集群行为空间。
- 在仿真中发现多种新行为，且可直接部署到真实机器人。
- 适合对机器人集群智能与现实部署感兴趣的开发者。
针对能力有限的机器人集群，本文提出一种基于自监督表征学习的Real2Sim2Real行为发现方法，可自动在仿真中发现潜在的涌现行为，并实现控制器直接迁移到真实机器人。首先，在仿真环境中验证该方法优于以往依赖人工设计指标的行为表示方式，能更准确刻画行为空间。其次，通过融合近期群体机器人的模拟到现实迁移技术，优化轻量级仿真器设计，有效缩小现实差距。最终，所有在仿真中发现的行为均成功部署于开源、低成本机器人平台，实现了从仿真到真实世界的无缝迁移。
原文摘要 · Abstract (English)
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.
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