表現学習とReal2Sim2Real転送による創発的なロボット群行動の発見と展開
Discovery and Deployment of Emergent Robot Swarm Behaviors via Representation Learning and Real2Sim2Real Transfer
自己教師あり表現学習と新規性探索を組み合わせ、シミュレーション上でロボット群の創発行動を自動発見し、実機へ直接転送する手法を提案した。
著者: Connor Mattson, Varun Raveendra, Ricardo Vega, Cameron Nowzari, Daniel S. Drew, Daniel S. Brown
分類: cs.RO, cs.AI, cs.MA
原文アブストラクト
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.
