Intrinsic-Motivation Multi-Robot Social Formation Navigation with Coordinated Exploration

• URL: http://arxiv.org/abs/2512.13293v2
• Date: Tue, 16 Dec 2025 03:34:39 GMT
• Title: Intrinsic-Motivation Multi-Robot Social Formation Navigation with Coordinated Exploration
• Authors: Hao Fu, Wei Liu, Shuai Zhou,
• Abstract summary: We propose a novel coordinated-exploration multi-robot RL algorithm.<n>Its core component is a self-learning intrinsic reward mechanism designed to collectively alleviate policy conservatism.<n> Empirical results on social formation navigation benchmarks demonstrate the proposed algorithm's superior performance.
• Score: 7.50564221243905
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper investigates the application of reinforcement learning (RL) to multi-robot social formation navigation, a critical capability for enabling seamless human-robot coexistence. While RL offers a promising paradigm, the inherent unpredictability and often uncooperative dynamics of pedestrian behavior pose substantial challenges, particularly concerning the efficiency of coordinated exploration among robots. To address this, we propose a novel coordinated-exploration multi-robot RL algorithm introducing an intrinsic motivation exploration. Its core component is a self-learning intrinsic reward mechanism designed to collectively alleviate policy conservatism. Moreover, this algorithm incorporates a dual-sampling mode within the centralized training and decentralized execution framework to enhance the representation of both the navigation policy and the intrinsic reward, leveraging a two-time-scale update rule to decouple parameter updates. Empirical results on social formation navigation benchmarks demonstrate the proposed algorithm's superior performance over existing state-of-the-art methods across crucial metrics. Our code and video demos are available at: https://github.com/czxhunzi/CEMRRL.

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