Swarm-Gen: Fast Generation of Diverse Feasible Swarm Behaviors

• URL: http://arxiv.org/abs/2501.19042v1
• Date: Fri, 31 Jan 2025 11:13:09 GMT
• Title: Swarm-Gen: Fast Generation of Diverse Feasible Swarm Behaviors
• Authors: Simon Idoko, B. Bhanu Teja, K. Madhava Krishna, Arun Kumar Singh,
• Abstract summary: Coordination behavior in robot swarms is inherently multi-modal in nature.<n>In this paper, we combine generative models with a safety-filter (SF) to generate diverse and feasible swarm behaviors.<n>We show that we can generate a large set of multi-modal, feasible trajectories, simulating diverse swarm behaviors, within a few tens of milliseconds.
• Score: 9.77508443944225
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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