---
title: "RoboVineSim: A Simulation Tool for Human-Robot Collaboration in Vineyard Harvesting"
authors:
  - "Dimitrios Troullinos"
  - "Maria Nuria Conejero"
  - "Filippo Bistaffa"
  - "Jose M. Bengochea-Guevara"
  - "Ángela Ribeiro"
  - "Juan A. Rodriguez-Aguilar"
doi: "10.24963/ijcai.2026/999"
url: "https://ijcai.org/proceedings/2026/999"
pdf: "https://ijcai.org/proceedings/2026/0999.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "8543-8546"
track: "Demo Track"
keywords:
  - "AI: Agent-based and Multi-agent Systems"
  - "AI: Multidisciplinary Topics and Applications"
  - "AI: Constraint Satisfaction and Optimization"
---

# RoboVineSim: A Simulation Tool for Human-Robot Collaboration in Vineyard Harvesting

Dimitrios Troullinos, Maria Nuria Conejero, Filippo Bistaffa, Jose M. Bengochea-Guevara, Ángela Ribeiro, Juan A. Rodriguez-Aguilar

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 8543-8546. DOI: [10.24963/ijcai.2026/999](https://doi.org/10.24963/ijcai.2026/999)

## Abstract

In agricultural tasks, manual grape harvesting remains a labor-intensive activity facing challenges of efficiency, labor shortages, and sustainability. To this end, tailor-made robotic systems have been designed with the capabilities to transfer heavy boxes, navigate vineyard terrains, communicate, accurately locate, and safely interact with humans. The introduction of collaborative robotic fleets alongside human workers in large-scale vineyard harvesting effectively presents a Multi-Robot Task Allocation (MRTA) problem, where the real-world domain possesses characteristics that, when combined, pose a challenging research endeavor and align with open issues in MRTA research. Here, we present RoboVineSim, a simulation tool that can capture any vineyard area using geographical data and model the behavior of humans and robots in the environment. In addition, we have established the necessary mechanisms to facilitate the development of novel MRTA methods in this domain.