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)

Swarm robotics localization: comparing methods from infrared to foundation models

Ali Imran

Vivek Shankar Vardharajan

Rafael Gomes Braga

David St-Onge

2026-02-01

Swarm Intelligence (published)

Revisiting the Learning Objectives of Vision-Language Reward Models

Simon Roy

Samuel Barbeau

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)

Attention-Based Multi-Agent RL for Multi-Machine Tending Using Mobile Robots

Abdalwhab Bakheet Mohamed Abdalwhab

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.

2025-09-30

AI (published)

Personalizing brain stimulation: continual learning for sleep spindle detection

Milo Sobral

Hugo R Jourde

S Ehsan M Bajestani

Emily B J Coffey

Objective. Personalized stimulation, in which algorithms used to detect neural events adapt to a user’s unique neural characteristics, may… (see more) be crucial to enable optimized and consistent stimulation quality for both fundamental research and clinical applications. Precise stimulation of sleep spindles-transient patterns of brain activity that occur during non rapid eye movement sleep that are involved in memory consolidation-presents an exciting frontier for studying memory functions; however, this endeavour is challenged by the spindles’ fleeting nature, inter-individual variability, and the necessity of real-time detection. Approach. We tackle these challenges using a novel continual learning framework. Using a pre-trained model capable of both online classification of sleep stages and spindle detection, we implement an algorithm that refines spindle detection, tailoring it to the individual throughout one or more nights without manual intervention. Main results. Our methodology achieves accurate, subject-specific targeting of sleep spindles and enables advanced closed-loop stimulation studies. While fine-tuning alone offers minimal benefits for single nights, our approach combining weight averaging demonstrates significant improvement over multiple nights, effectively mitigating catastrophic forgetting. Significance. This work represents an important step towards signal-level personalization of brain stimulation that can be applied to different brain stimulation paradigms including closed-loop brain stimulation, and to different neural events. Applications in fundamental neuroscience may enhance the investigative potential of brain stimulation to understand cognitive processes such as the role of sleep spindles in memory consolidation, and may lead to novel therapeutic applications.

2025-07-02

Journal of Neural Engineering (published)

GNN-based Decentralized Perception in Multirobot Systems for Predicting Worker Actions

Ali Imran

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.

2025-05-31

IEEE Robotics and Automation Letters (published)

Neurophysiological effects of targeting sleep spindles with closed-loop auditory stimulation

Hugo R Jourde

Milo Sobral

Emily B J Coffey

Sleep spindles are neural events unique to nonrapid eye movement sleep that play key roles in memory reactivation and consolidation. However… (see more), much of the evidence for their function remains correlational rather than causal. Closed-loop brain stimulation uses real-time monitoring of neural events (often via electroencephalography; EEG) to deliver precise auditory, magnetic, or electrical stimulation for research or therapeutic purposes. Automated online algorithms to detect and stimulate sleep spindles have recently been validated, but the time- and frequency-resolved physiological responses generated by them have not yet been documented. Building on the recent findings that sleep spindles do not block the transmission of sound to cortex, the present work investigates the neurophysiological responses to closed-loop auditory stimulation of sleep spindles. EEG data were collected from 10 healthy human adults (6 nights each), whilst sleep spindles were detected and in half the nights, targeted with auditory stimulation. Spindles were successfully stimulated before their offset in 97.6% of detections and did not disturb sleep. Comparing stimulation with sham, we observed that stimulation resulted in increased sigma activity (11–16 Hz) at about 1 second poststimulation but that stimulation occurring at the beginning of the spindle also resulted in early termination of the spindle. Finally, we observed that stimulating an evoked spindle did not elicit additional sigma activity. Our results validate the use of closed-loop auditory stimulation targeting sleep spindles, and document its neural effects, as a basis for future causal investigations concerning spindles’ roles in memory consolidation.

2025-03-31

Sleep Advances (published)

Learning Multi-agent Multi-machine Tending by Mobile Robots

Abdalwhab Abdalwhab

S 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.

2025-02-24

AAAI.org/2025/Workshop/MARW (published)

openreview.net

A Blockchain Framework for Equitable and Secure Task Allocation in Robot Swarms

Hanqing Zhao

Alexandre Pacheco

Xue 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.

2024-12-31

IEEE Robotics and Automation Letters (unknown)

3D Foundation Model-Based Loop Closing for Decentralized Collaborative SLAM

Pierre-Yves Lajoie

Benjamin Ramtoula

Daniele De Martini

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.

2024-12-31

IEEE Robotics Autom. Lett. (published)

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 Varadharajan

Decentralized collaborative simultaneous localization and mapping (C-SLAM) is essential to enable multirobot missions in unknown environment… (see more)s without relying on preexisting localization and communication infrastructure. This technology is anticipated to play a key role in the exploration of the Moon, Mars, and other planets. In this article, we share insights and lessons learned from C-SLAM experiments involving three robots operating on a Mars analogue terrain and communicating over an ad hoc network. We examine the impact of limited and intermittent communication on C-SLAM performance, as well as the unique localization challenges posed by planetary-like environments. Additionally, we introduce a novel dataset collected during our experiments, which includes real-time peer-to-peer inter-robot throughput and latency measurements. This dataset aims to support future research on communication-constrained, decentralized multirobot operations.

2024-12-31

IEEE Transactions on Field Robotics (published)

A Multi-Robot Exploration Planner for Space Applications

Vivek Shankar Vardharajan

We propose a distributed multi-robot exploration planning method designed for complex, unconstrained environments featuring steep elevation … (see more)changes. The method employs a two-tiered approach: a local exploration planner that constructs a grid graph to maximize exploration gain and a global planner that maintains a sparse navigational graph to track visited locations and frontier information. The global graphs are periodically synchronized among robots within communication range to maintain an updated representation of the environment. Our approach integrates localization loop closure estimates to correct global graph drift. In simulation and field tests, the proposed method achieves 50% lower computational runtime compared to state-of-the-art methods while demonstrating superior exploration coverage. We evaluate its performance in two simulated subterranean environments and in field experiments at a Mars-analog terrain.

2024-12-31

IEEE Robotics and Automation Letters (published)