Discovery and Deployment of Emergent Robot Swarm Behaviors via Representation Learning and Real2Sim2Real Transfer

2025-02-21

AI Summary

Emerging behaviors in resource-constrained micro-robotic swarms remain difficult to discover and deploy autonomously due to the absence of human feedback or hand-crafted behavioral metrics. Method: We propose a fully automated framework for emergence discovery and deployment, integrating self-supervised representation learning to drive novelty search for efficient behavioral space exploration, and a lightweight Real2Sim2Real transfer mechanism enabling zero-parameter, end-to-end deployment of simulated behaviors onto open-source, low-cost hardware. Contributions/Results: Key techniques include self-supervised representation learning, novelty search, lightweight physics-based simulation, sim-to-real transfer, and distributed controller compression and generalization. Experiments demonstrate significantly improved coverage and discriminability of the behavioral space in simulation; all discovered behaviors successfully transfer to real micro-robot swarms without retraining or human intervention—achieving, for the first time, a fully autonomous closed-loop emergence pipeline tailored to resource-limited platforms.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

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.

Problem

• Automate discovery of emergent robot swarm behaviors
• Bridge simulation to real robot deployment gap
• Enhance behavior representation via self-supervised learning

Innovation

• Self-Supervised Representation Learning
• Real2Sim2Real Transfer
• Novelty Search