Publications

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… (voir plus)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.

2025-11-01

IEEE Robotics and Automation Letters (publié)

doi.org

The role of Large Language Models in IoT security: A systematic review of advances, challenges, and opportunities

Saeid Jamshidi

Negar Shahabi

Amin Nikanjam

Kawser Wazed Nafi

Foutse Khomh

Carol Fung

2025-11-01

Internet of Things (publié)

doi.org

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

Alexandre Pacheco

Marco Dorigo

Recent studies demonstrate the potential of blockchain to enable robots in a swarm to achieve secure consensus about the environment, partic… (voir plus)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 (publié)

doi.org

Intersecting perspectives: A participatory street review framework for urban inclusivity

Rashid A. Mushkani

Shin (Alexandre) Koseki

2025-10-01

Habitat International (publié)

doi.org

Intersecting perspectives: A participatory street review framework for urban inclusivity

Rashid A. Mushkani

Shin (Alexandre) Koseki

2025-10-01

Habitat International (publié)

doi.org

Intersecting perspectives: A participatory street review framework for urban inclusivity

Rashid A. Mushkani

Shin (Alexandre) Koseki

2025-10-01

Habitat International (publié)

doi.org

They Hear Me Rolling: Design and Characterization of a Distributed, Rolling Acoustic-Tactile Sensor

Wilfred Mason

David Brenken

Olivier St-Martin Cormier

Audrey Sedal

Tactile sensor design has been widely explored at the centimeter-scale; fewer explorations exist in larger scale systems with varied geometr… (voir plus)ies. We present a meter-scale tactile sensor for wheeled robotic platforms based on a flexible acoustic waveguide. This sensor architecture performs contact sensing over the surface of a rotating wheel with a single transducer that is separated from the sensing surface. The design and characterization of the sensor are presented, along with a demonstration of a state-estimation framework using tactile sensor feedback to measure surface features.

2025-10-01

IEEE Sensors Letters (publié)

doi.org

AInstein: Can AI Rediscover Scientific Concepts from First Principles?

Shambhavi Mishra

Jose Dolz

Large language models have demonstrated remarkable capabilities across diverse tasks, yet a fundamental question remains: can these models g… (voir plus)enuinely rediscover complex scientific insights, or do they merely recite memorized information? We present AInstein, a novel framework for evaluating whether language models can derive established scientific concepts from first principles when stripped of domain-specific terminology. Rather than testing the recall of scientific facts, we reformulate landmark discoveries as conceptual puzzles, challenging models to reconstruct the underlying technical solutions independently.

2025-09-22

NeurIPS.cc/2025/Workshop/WiML (publié)

openreview.net

Conditional Adversarial Random Forest for Synthetic Electronic Health Record Generation

Cynthia García Ybarra

Christian Gagné

2025-09-22

NeurIPS.cc/2025/Workshop/WiML (publié)

openreview.net

Graph Dreamer: Temporal Graph World Models for Sample-Efficient and Generalisable Reinforcement Learning

Anaïs Berkes

Donna Vakalis

Yoshua Bengio

David Rolnick

2025-09-22

NeurIPS.cc/2025/Workshop/WiML (publié)

openreview.net

Intrinsic Meets Extrinsic Fairness: Assessing the Downstream Impact of Bias Mitigation in Large Language Models

Mina Arzaghi

Alireza Dehghanpour Farashah

Florian Carichon

Golnoosh Farnadi

Large Language Models (LLMs) are increasingly deployed in sensitive domains such as finance, where intrinsic representational biases can pro… (voir plus)pagate into extrinsic harms in downstream tasks. High-stakes applications such as credit scoring are especially vulnerable, as biased model behavior can reinforce existing inequities and result in harmful disparities across demographic groups \cite{blodgett2020language}. While prior research has questioned whether intrinsic bias truly translates into extrinsic unfairness \cite{goldfarb2020intrinsic}, this connection remains poorly understood. To address this gap, we propose a four-stage evaluation framework that systematically examines the relationship between intrinsic and extrinsic fairness. In Stage 1, we establish a baseline by training models such as logistic regression, LLM embeddings, and fine-tuned classifiers without any mitigation strategy, providing reference points for fairness and accuracy. In Stage 2, we evaluate task-level mitigation through Counterfactual Data Augmentation (CDA) \cite{gallegos2024bias}, which balances gender representation by generating counterfactual training instances, allowing us to assess improvements in extrinsic fairness. In Stage 3, we adapt concept unlearning \cite{dige2024mitigating} as an intrinsic bias mitigation method, encouraging LLMs to forget socioeconomic stereotypes while preserving fluency and predictive utility, and we evaluate how this intervention impacts downstream fairness. Finally, in Stage 4, we combine CDA with unlearning to test whether dual mitigation further enhances fairness. We conduct experiments on three datasets (Adult Census Income, ACS Employment, and German Credit) using instruction-tuned LLMs (LLaMA-3.1, Phi-3, and Gemma-2) in both frozen embedding and fine-tuned classifier settings, evaluating performance with predictive accuracy and group fairness metrics, including Demographic Parity, Accuracy Parity, and Equality of Odds. Our experiments demonstrate that intrinsic bias mitigation through unlearning is highly effective; in Phi-3, for instance, it reduces gender socioeconomic stereotype gaps by 94.9\% while maintaining language fluency. In downstream tasks, unlearning consistently improves group fairness metrics while preserving predictive accuracy, whereas CDA primarily enhances demographic parity but can introduce accuracy trade-offs. For instance, on the ACS Employment dataset, unlearned Gemma-2 improved Accuracy Parity from 0.199 to 0.104 (48\% gain), and combining CDA with unlearning on Llama-3.1 reduced Demographic Parity from 0.080 to 0.014 (82\% gain). On the Adult dataset, all three models maintained accuracy above 0.82 while showing reduced fairness gaps, and on German Credit, unlearning consistently outperformed CDA by improving group fairness metrics without sacrificing predictive performance. Overall, CDA and unlearning exhibit complementary effects, with their combination yielding the strongest fairness improvements across models and datasets. This work contributes to bias mitigation and fairness in LLMs in two ways. First, we adapt concept unlearning to mitigate socioeconomic stereotyping, showing that intrinsic bias reduction improves both representational and downstream fairness. Second, we introduce a unified evaluation framework that links intrinsic and extrinsic fairness, enabling systematic comparison of mitigation strategies. The framework is flexible, applying to both fine-tuned and frozen LLMs, and offers actionable guidance for deploying fairer models in finance and other high-stakes domains.

2025-09-22

NeurIPS.cc/2025/Workshop/WiML (publié)

openreview.net