Portrait of Giovanni Beltrame

Giovanni Beltrame

Affiliate Member
Full Professor, Polytechnique Montréal, Department of Computer Engineering and Software Engineering
Research Topics
Autonomous Robotics Navigation
Computer Vision
Distributed Systems
Human-Robot Interaction
Online Learning
Reinforcement Learning
Robotics
Swarm Intelligence
Website
Google Scholar
LinkedIn

Biography

Giovanni Beltrame obtained his PhD in computer engineering from Politecnico di Milano in 2006, after which he worked as a microelectronics engineer at the European Space Agency on a number of projects, from radiation-tolerant systems to computer-aided design.

In 2010, he moved to Montréal, where he is currently a professor at Polytechnique Montréal in the Computer and Software Engineering Department.

Beltrame directs the Making Innovative Space Technology (MIST) Lab, where he has more than twenty-five students and postdocs under his supervision. He has completed several projects in collaboration with industry and government agencies in the area of robotics, disaster response and space exploration. He and his team have participated in several field missions with ESA, the Canadian Space Agency (CSA) and NASA, including BRAILLE, PANAGAEA-X and IGLUNA.

His research interests include the modelling and design of embedded systems, AI and robotics, and he has published his findings in top journals and conferences.

Current Students

Haechan Mark Bong
PhD - Polytechnique Montréal
Co-supervisor :
Jana Pavlasek
Website
Google Scholar
Yann Bouteiller
Collaborating researcher - Polytechnique Montréal Montreal
Co-supervisor :
Jana Pavlasek
Website
Github
Google Scholar
Théo Gachet
Master's Research - Polytechnique Montréal
Co-supervisor :
Jana Pavlasek
Website
Github
Maeva Guerrier
PhD - Polytechnique Montréal
Co-supervisor :
Jana Pavlasek
Github
Simon Roy
Master's Research - Université de Montréal
Co-supervisor :
Jana Pavlasek
Website
Github
Soma Soma
PhD - Polytechnique Montréal
Co-supervisor :
Jana Pavlasek

Publications

Scalable Multi-Agent Reinforcement Learning Framework for Multi-Machine Tending
Abdalwhab Abdalwhab
Giovanni Beltrame
David St-Onge
Robotic manipulators hold significant untapped potential for manufacturing industries, particularly when deployed in multi-robot configurati… (see more)ons that can enhance resource utilization, increase throughput, and reduce costs. However, industrial manipulators typically operate in isolated one-robot, one-machine setups, limiting both utilization and scalability. Even mobile robot implementations generally rely on centralized architectures, creating vulnerability to single points of failure and requiring robust communication infrastructure. This paper introduces SMAPPO (Scalable Multi-Agent Proximal Policy Optimization), a scalable input-size invariant multi-agent reinforcement learning model for decentralized multi-robot management in industrial environments. MAPPO (Multi-Agent Proximal Policy Optimization) represents the current state-of-the-art approach. We optimized an existing simulator to handle complex multi-agent reinforcement learning scenarios and designed a new multi-machine tending scenario for evaluation. Our novel observation encoder enables SMAPPO to handle varying numbers of agents, machines, and storage areas with minimal or no retraining. Results demonstrate SMAPPO's superior performance compared to the state-of-the-art MAPPO across multiple conditions: full retraining (up to 61% improvement), curriculum learning (up to 45% increased productivity and up to 49% fewer collisions), zero-shot generalization to significantly different scale scenarios (up to 272% better performance without retraining), and adaptability under extremely low initial training (up to 100% increase in parts delivery).
2026-02-28
IEEE Robotics and Automation Letters (published)
doi.org
Swarm robotics localization: comparing methods from infrared to foundation models
Ali Imran
Vivek Shankar Vardharajan
Rafael Gomes Braga
Giovanni Beltrame
David St-Onge
2026-02-01
Swarm Intelligence (published)
doi.org
Revisiting the Learning Objectives of Vision-Language Reward Models
Simon Roy
Samuel Barbeau
Giovanni Beltrame
Christian Desrosiers
Nicolas Thome
Learning generalizable reward functions is a core challenge in embodied intelligence. Recent work leverages contrastive vision language mode… (see more)ls (VLMs) to obtain dense, domain-agnostic rewards without human supervision. These methods adapt VLMs into reward models through increasingly complex learning objectives, yet meaningful comparison remains difficult due to differences in training data, architectures, and evaluation settings. In this work, we isolate the impact of the learning objective by evaluating recent VLM-based reward models under a unified framework with identical backbones, finetuning data, and evaluation environments. Using Meta-World tasks, we assess modeling accuracy by measuring consistency with ground truth reward and correlation with expert progress. Remarkably, we show that a simple triplet loss outperforms state-of-the-art methods, suggesting that much of the improvements in recent approaches could be attributed to differences in data and architectures.
2025-11-30
arXiv (published)
doi.org
arxiv.org
Attention-Based Multi-Agent RL for Multi-Machine Tending Using Mobile Robots
Abdalwhab Abdalwhab
Giovanni Beltrame
Samira Ebrahimi Kahou
David St-Onge
Robotics can help address the growing worker shortage challenge of the manufacturing industry. As such, machine tending is a task collaborat… (see more)ive robots can tackle that can also greatly boost productivity. Nevertheless, existing robotics systems deployed in that sector rely on a fixed single-arm setup, whereas mobile robots can provide more flexibility and scalability. We introduce a multi-agent multi-machine-tending learning framework using mobile robots based on multi-agent reinforcement learning (MARL) techniques, with the design of a suitable observation and reward. Moreover, we integrate an attention-based encoding mechanism into the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm to boost its performance for machine-tending scenarios. Our model (AB-MAPPO) outperforms MAPPO in this new challenging scenario in terms of task success, safety, and resource utilization. Furthermore, we provided an extensive ablation study to support our design decisions.
2025-10-01
Applied Informatics (published)
doi.org
Attention-Based Multi-Agent RL for Multi-Machine Tending Using Mobile Robots
Abdalwhab Bakheet Mohamed Abdalwhab
Giovanni Beltrame
Samira Ebrahimi Kahou
David St-Onge
2025-10-01
AI (published)
doi.org
Attention-Based Multi-Agent RL for Multi-Machine Tending Using Mobile Robots
Abdalwhab Abdalwhab
Giovanni Beltrame
Samira Ebrahimi Kahou
David St-Onge
Robotics can help address the growing worker shortage challenge of the manufacturing industry. As such, machine tending is a task collaborat… (see more)ive robots can tackle that can also greatly boost productivity. Nevertheless, existing robotics systems deployed in that sector rely on a fixed single-arm setup, whereas mobile robots can provide more flexibility and scalability. We introduce a multi-agent multi-machine-tending learning framework using mobile robots based on multi-agent reinforcement learning (MARL) techniques, with the design of a suitable observation and reward. Moreover, we integrate an attention-based encoding mechanism into the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm to boost its performance for machine-tending scenarios. Our model (AB-MAPPO) outperforms MAPPO in this new challenging scenario in terms of task success, safety, and resource utilization. Furthermore, we provided an extensive ablation study to support our design decisions.
2025-10-01
AI (published)
doi.org
A Blockchain Framework for Equitable and Secure Task Allocation in Robot Swarms
Hanqing Zhao
Alexandre Pacheco
Giovanni Beltrame
Xue (Steve) Liu
Marco Dorigo
Gregory Dudek
Recent studies demonstrate the potential of blockchain to enable robots in a swarm to achieve secure consensus about the environment, partic… (see more)ularly when robots are homogeneous and perform identical tasks. Typically, robots receive rewards for their contributions to consensus achievement, but no studies have yet targeted heterogeneous swarms, in which the robots have distinct physical capabilities suited to different tasks. We present a novel framework that leverages domain knowledge to decompose the swarm mission into a hierarchy of tasks within smart contracts. This allows the robots to reach a consensus about both the environment and the action plan, allocating tasks among robots with diverse capabilities to improve their performance while maintaining security against faults and malicious behaviors. We refer to this concept as equitable and secure task allocation. Validated in Simultaneous Localization and Mapping missions, our approach not only achieves equitable task allocation among robots with varying capabilities, improving mapping accuracy and efficiency, but also shows resilience against malicious attacks.
2025-10-01
IEEE Robotics and Automation Letters (published)
doi.org
GNN-based Decentralized Perception in Multirobot Systems for Predicting Worker Actions
Ali Imran
Giovanni Beltrame
David St-Onge
In industrial environments, predicting human actions is essential for ensuring safe and effective collaboration between humans and robots. T… (see more)his paper introduces a perception framework that enables mobile robots to understand and share information about human actions in a decentralized way. The framework first allows each robot to build a spatial graph representing its surroundings, which it then shares with other robots. This shared spatial data is combined with temporal information to track human behavior over time. A swarm-inspired decision-making process is used to ensure all robots agree on a unified interpretation of the human's actions. Results show that adding more robots and incorporating longer time sequences improve prediction accuracy. Additionally, the consensus mechanism increases system resilience, making the multi-robot setup more reliable in dynamic industrial settings.
2025-06-01
IEEE Robotics and Automation Letters (published)
doi.org
arxiv.org
Learning Multi-agent Multi-machine Tending by Mobile Robots
Abdalwhab Abdalwhab
Giovanni Beltrame
Samira Ebrahimi Kahou
David St-Onge
Robotics can help address the growing worker shortage challenge of the manufacturing industry. As such, machine tending is a task collaborat… (see more)ive robots can tackle that can also highly boost productivity. Nevertheless, existing robotics systems deployed in that sector rely on a fixed single-arm setup, whereas mobile robots can provide more flexibility and scalability. In this work, we introduce a multi-agent multi-machine tending learning framework by mobile robots based on Multi-agent Reinforcement Learning (MARL) techniques with the design of a suitable observation and reward. Moreover, an attention-based encoding mechanism is developed and integrated into Multi-agent Proximal Policy Optimization (MAPPO) algorithm to boost its performance for machine tending scenarios. Our model (AB-MAPPO) outperformed MAPPO in this new challenging scenario in terms of task success, safety, and resources utilization. Furthermore, we provided an extensive ablation study to support our various design decisions.
2025-02-25
AAAI.org/2025/Workshop/MARW (published)
doi.org
openreview.net
GNN-based Decentralized Perception in Multirobot Systems for Predicting Worker Actions
Ali Imran
Giovanni Beltrame
David St-Onge
In industrial environments, predicting human actions is essential for ensuring safe and effective collaboration between humans and robots. T… (see more)his paper introduces a perception framework that enables mobile robots to understand and share information about human actions in a decentralized way. The framework first allows each robot to build a spatial graph representing its surroundings, which it then shares with other robots. This shared spatial data is combined with temporal information to track human behavior over time. A swarm-inspired decision-making process is used to ensure all robots agree on a unified interpretation of the human's actions. Results show that adding more robots and incorporating longer time sequences improve prediction accuracy. Additionally, the consensus mechanism increases system resilience, making the multi-robot setup more reliable in dynamic industrial settings.
2025-01-08
ArXiv (preprint)
arxiv.org
arxiv.org
3D Foundation Model-Based Loop Closing for Decentralized Collaborative SLAM
Pierre-Yves Lajoie
Benjamin Ramtoula
Daniele De Martini
Giovanni Beltrame
Decentralized Collaborative Simultaneous Localization and Mapping (C-SLAM) techniques often struggle to identify map overlaps due to signifi… (see more)cant viewpoint variations among robots. Motivated by recent advancements in 3D foundation models, which can register images despite large viewpoint differences, we propose a robust loop closing approach that leverages these models to establish inter-robot measurements. In contrast to resource-intensive methods requiring full 3D reconstruction within a centralized map, our approach integrates foundation models into existing SLAM pipelines, yielding scalable and robust multi-robot mapping. Our contributions include: 1) integrating 3D foundation models to reliably estimate relative poses from monocular image pairs within decentralized C-SLAM; 2) introducing robust outlier mitigation techniques critical to the use of these relative poses and 3) developing specialized pose graph optimization formulations that efficiently resolve scale ambiguities. We evaluate our method against state-of-the-art approaches, demonstrating improvements in localization and mapping accuracy, alongside significant gains in computational and memory efficiency. These results highlight the potential of our approach for deployment in large-scale multi-robot scenarios.
2025-01-01
IEEE Robotics Autom. Lett. (published)
doi.org
Multi-Robot Decentralized Collaborative SLAM in Planetary Analogue Environments: Dataset, Challenges, and Lessons Learned
Pierre-Yves Lajoie
Karthik Soma
Haechan Mark Bong
Alice Lemieux-Bourque
Rongge Zhang
Vivek Shankar Vardharajan
Giovanni Beltrame
2025-01-01
IEEE Transactions on Field Robotics (published)
doi.org