Efficient Environment Design for Multi-Robot Navigation via Continuous Control

2025-08-17

AI Summary

Sample inefficiency and lack of formal guarantees hinder multi-robot navigation and path planning in continuous state-action spaces under uncertainty. Method: This paper proposes an efficient, customizable simulation environment modeling the task as a Markov decision process (MDP) with the objective of minimizing path cost to regions of interest. It introduces the first formal environment design framework tailored for multi-robot navigation—ensuring policy verifiability while improving sample efficiency. The environment supports diverse RL algorithms, including gradient-based methods (A2C, PPO, TRPO, TQC, CrossQ) and gradient-free optimization (ARS), and is validated for robustness in a 3D agricultural scenario using CoppeliaSim. Contribution/Results: Experiments demonstrate significantly reduced training time, strong generalization across environments and robot configurations, and effective policy learning and deployment under resource-constrained conditions.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Multi-robot navigation and path planning in continuous state and action spaces with uncertain environments remains an open challenge. Deep Reinforcement Learning (RL) is one of the most popular paradigms for solving this task, but its real-world application has been limited due to sample inefficiency and long training periods. Moreover, the existing works using RL for multi-robot navigation lack formal guarantees while designing the environment. In this paper, we introduce an efficient and highly customizable environment for continuous-control multi-robot navigation, where the robots must visit a set of regions of interest (ROIs) by following the shortest paths. The task is formally modeled as a Markov Decision Process (MDP). We describe the multi-robot navigation task as an optimization problem and relate it to finding an optimal policy for the MDP. We crafted several variations of the environment and measured the performance using both gradient and non-gradient based RL methods: A2C, PPO, TRPO, TQC, CrossQ and ARS. To show real-world applicability, we deployed our environment to a 3-D agricultural field with uncertainties using the CoppeliaSim robot simulator and measured the robustness by running inference on the learned models. We believe our work will guide the researchers on how to develop MDP-based environments that are applicable to real-world systems and solve them using the existing state-of-the-art RL methods with limited resources and within reasonable time periods.

Problem

• Multi-robot navigation in continuous uncertain environments
• Sample inefficiency in deep reinforcement learning training
• Lacking formal guarantees in RL environment design

Innovation

• Continuous-control multi-robot navigation environment
• MDP-based optimization with formal guarantees
• Gradient and non-gradient RL methods tested