# Multi-Robot Cooperative Navigation in Crowds: A Game-Theoretic Learning-Based Model Predictive Control Approach

arXiv: https://arxiv.org/abs/2310.06964 · HTML: https://mlsanity.com/paper/2310.06964/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2023.

## Built on by (most cited first, 5 of 5)

- [Generalized Mission Planning for Heterogeneous Multi-Robot Teams via LLM-constructed Hierarchical Trees](https://mlsanity.com/paper/2501.16539/) (2025)
- [In Search of a Lost Metric: Human Empowerment as a Pillar of Socially Conscious Navigation](https://mlsanity.com/paper/2501.01539/) (2025)
- [Communication-Efficient Distributed Learning with Differential Privacy](https://mlsanity.com/paper/2604.02558/) (2026)
- [Intrinsic-Motivation Multi-Robot Social Formation Navigation with Coordinated Exploration](https://mlsanity.com/paper/2512.13293/) (2025)
- [GIANT - Global Path Integration and Attentive Graph Networks for Multi-Agent Trajectory Planning](https://mlsanity.com/paper/2603.04659/) (2026)

## Abstract (arXiv)

In this paper, we develop a control framework for the coordination of multiple robots as they navigate through crowded environments. Our framework comprises of a local model predictive control (MPC) for each robot and a social long short-term memory model that forecasts pedestrians' trajectories. We formulate the local MPC formulation for each individual robot that includes both individual and shared objectives, in which the latter encourages the emergence of coordination among robots. Next, we consider the multi-robot navigation and human-robot interaction, respectively, as a potential game and a two-player game, then employ an iterative best response approach to solve the resulting optimization problems in a centralized and distributed fashion. Finally, we demonstrate the effectiveness of coordination among robots in simulated crowd navigation.