Swarm-Gen: Fast Generation of Diverse Feasible Swarm Behaviors

TL;DR

Swarm-Gen introduces a scalable method combining generative models and safety filters to rapidly produce diverse, feasible swarm behaviors, enhancing the control and coordination of robot swarms.

Contribution

It presents a novel approach integrating generative models with a safety filter and a neural network-based initialization to generate diverse swarm trajectories efficiently.

Findings

Generated diverse trajectories within tens of milliseconds.

VQ-VAE offers better diversity-computation trade-offs.

Initialization network accelerates safety filter convergence.

Abstract

Coordination behavior in robot swarms is inherently multi-modal in nature. That is, there are numerous ways in which a swarm of robots can avoid inter-agent collisions and reach their respective goals. However, the problem of generating diverse and feasible swarm behaviors in a scalable manner remains largely unaddressed. In this paper, we fill this gap by combining generative models with a safety-filter (SF). Specifically, we sample diverse trajectories from a learned generative model which is subsequently projected onto the feasible set using the SF. We experiment with two choices for generative models, namely: Conditional Variational Autoencoder (CVAE) and Vector-Quantized Variational Autoencoder (VQ-VAE). We highlight the trade-offs these two models provide in terms of computation time and trajectory diversity. We develop a custom solver for our SF and equip it with a neural network that predicts context-specific initialization. Thecinitialization network is trained in a self-supervised manner, taking advantage of the differentiability of the SF solver. We provide two sets of empirical results. First, we demonstrate that we can generate a large set of multi-modal, feasible trajectories, simulating diverse swarm behaviors, within a few tens of milliseconds. Second, we show that our initialization network provides faster convergence of our SF solver vis-a-vis other alternative heuristics.