Multi-Robot Multitask Gaussian Process Estimation and Coverage

Lai Wei; Andrew McDonald; Vaibhav Srivastava

eess.SYMar 11, 2026

Multi-Robot Multitask Gaussian Process Estimation and Coverage

Authors: Lai Wei, Andrew McDonald, Vaibhav Srivastava

Organizations: Life Sciences Institute, University of Michigan, Ann Arbor, MI 48109 USA · University of Cambridge, Cambridge, UK. · Department of Electrical and Computer Engineering. Michigan State University, East Lansing, MI 48823 USA.

Abstract

Coverage control is essential for the optimal deployment of agents to monitor or cover areas with sensory demands. While traditional coverage involves single-task robots, increasing autonomy now enables multitask operations. This paper introduces a novel multitask coverage problem and addresses it for both the cases of known and unknown sensory demands. For known demands, we design a federated multitask coverage algorithm and establish its convergence properties. For unknown demands, we employ a multitask Gaussian Process (GP) framework to learn sensory demand functions and integrate it with the multitask coverage algorithm to develop an adaptive algorithm. We introduce a novel notion of multitask coverage regret that compares the performance of the adaptive algorithm against an oracle with prior knowledge of the demand functions. We establish that our algorithm achieves sublinear cumulative regret, and numerically illustrate its performance.

Figures & tables

\eta_{i}\in\argmin_{v\in V}\mathcal{H}_{\textup{inf}}(\boldsymbol{\eta}_{-i,v}).

Algorithm 1 Federated Multitask Coverage

\max_{i\in V}\tr\big(\boldsymbol{\tilde{\Sigma}}_{i}(k)\big)\leq\alpha^{\ell}\tau.

Algorithm 2 DSMLC

Fig. 1: True demand fields: (a) $\phi^{1}$ and (b) $\phi^{2}$ .

Fig. 2: Federated multitask coverage (FMTC) under known demand.

Fig. 3: Final deployment and multitask partition in the heterogeneous case. The red dots represent robots with superior Task 2 capability, and white dots represent other robots. The color of the partition is mapped to the robot identity with 0 and 10 indicating uncovered and multiply covered vertices, respectively. Robots with superior Task 2 capabilities have indices $\{1,3,6\}$ .

Fig. 4: Cumulative regret comparison in the single-task case.

Fig. 5: Cumulative regret comparison in the heterogeneous two-task case.

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Resilient Decentralized Ergodic Coverage for Scalable Multi-Robot Systems in Unknown Time-Varying Environments

Maria G. Mendoza, Victoria Marie Tuck, Chinmay Maheshwari, Shankar Sastry

Department of Mechanical Engineering, University of California, Berkeley, CA 94720, USA · Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA · Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21209, USA+1

Multi-Robot Systems Exploration-Exploitation Tradeoff Resilient Multi-Robot Coordination Multi-Robot Exploration Multi-Robot Coordination

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Maintaining situational awareness in high-stakes multi-robot applications requires balancing exploration of unobserved regions with sustained monitoring of changing Regions of Interest (ROIs), often under unknown and time-varying distributions, partial observability, and limited communication. We propose a decentralized multi-agent coverage framework that serves as a high-level planning strategy, in which each agent computes an adaptive ergodic policy, implemented via a Markov-chain, that tracks an updated belief over the underlying importance map. Beliefs are maintained online via Gaussian Process (GP) regression from local noisy observations exchanged with neighbors. The resulting policy drives agents to spend time in ROIs in proportion to their estimated importance, while preserving sufficient exploration to detect and adapt to time-varying environmental changes. Unlike existing approaches that assume known importance maps, centralized coordination, or a static environment, our framework addresses the combined challenges of unknown, time-varying distributions under a decentralized, partially observable setting. We further show that our framework is robust to communication and memory degradation, robot loss, and can scale up to hundreds of robots.

Abstract

Multi-Robot Coordination

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