Publications

Multimodal and Force-Matched Imitation Learning with a See-Through Visuotactile Sensor

Trevor Ablett

Oliver Limoyo

Adam Sigal

Affan Jilani

Jonathan Kelly

Kaleem Siddiqi

Francois Hogan

Gregory Dudek

Kinesthetic Teaching is a popular approach to collecting expert robotic demonstrations of contact-rich tasks for imitation learning (IL), bu… (voir plus)t it typically only measures motion, ignoring the force placed on the environment by the robot. Furthermore, contact-rich tasks require accurate sensing of both reaching and touching, which can be difficult to provide with conventional sensing modalities. We address these challenges with a See-Through-your-Skin (STS) visuotactile sensor, using the sensor both (i) as a measurement tool to improve kinesthetic teaching, and (ii) as a policy input in contact-rich door manipulation tasks. An STS sensor can be switched between visual and tactile modes by leveraging a semi-transparent surface and controllable lighting, allowing for both pre-contact visual sensing and during-contact tactile sensing with a single sensor. First, we propose tactile force matching, a methodology that enables a robot to match forces read during kinesthetic teaching using tactile signals. Second, we develop a policy that controls STS mode switching, allowing a policy to learn the appropriate moment to switch an STS from its visual to its tactile mode. Finally, we study multiple observation configurations to compare and contrast the value of visual and tactile data from an STS with visual data from a wrist-mounted eye-in-hand camera. With over 3,000 test episodes from real-world manipulation experiments, we find that the inclusion of force matching raises average policy success rates by 62.5%, STS mode switching by 30.3%, and STS data as a policy input by 42.5%. Our results highlight the utility of see-through tactile sensing for IL, both for data collection to allow force matching, and for policy execution to allow accurate task feedback.

2024-12-31

IEEE Transactions on Robotics (publié)

doi.org

arxiv.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 Varadharajan

Giovanni Beltrame

Decentralized collaborative simultaneous localization and mapping (C-SLAM) is essential to enable multirobot missions in unknown environment… (voir plus)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 (publié)

doi.org

arxiv.org

Nteasee: A mixed methods study of expert and general population perspectives on deploying AI for health in African countries

Mercy Nyamewaa Asiedu

Iskandar Haykel

Awa Dieng

K. Kauer

Tousif Ahmed

Florence Ofori

Charisma Chan

Stephen R. Pfohl

Negar Rostamzadeh

Katherine Heller

2024-12-31

FAccT (publié)

doi.org

arxiv.org

Online Influence Campaigns: Strategies and Vulnerabilities

Andreea Musulan

Veronica Xia

Ethan Kosak-Hine

Tom Gibbs

Vidya Sujaya

Reihaneh Rabbany

Jean-François Godbout

Kellin Pelrine

U. Montr'eal

Ivado

M. University

In order to combat the creation and spread of harmful content online, this paper defines and contextualizes the concept of inauthentic, soci… (voir plus)etal-scale manipulation by malicious actors. We review the literature on societally harmful content and how it proliferates to analyze the manipulation strategies used by such actors and the vulnerabilities they target. We also provide an overview of three case studies of extensive manipulation campaigns to emphasize the severity of the problem. We then address the role that Artificial Intelligence plays in the development and dissemination of harmful content, and how its evolution presents new threats to societal cohesion for countries across the globe. Our survey aims to increase our understanding of not just particular aspects of these threats, but also the strategies underlying their deployment, so we can effectively prepare for the evolving cybersecurity landscape.

2024-12-31

SSRN Electronic Journal (accepté)

doi.org

arxiv.org

Open Technical Problems in Open-Weight AI Model Risk Management

Stephen Casper

Kyle O'Brien

Shayne Longpre

Elizabeth Seger

Kevin Klyman

Rishi Bommasani

Aniruddha Nrusimha

Ilia Shumailov

Sören Mindermann

Steven Basart

Frank Rudzicz

Kellin Pelrine

Avijit Ghosh

Andrew Strait

Robert Kirk

Dan Hendrycks

Peter Henderson

J. Zico Kolter

Geoffrey Irving

Yarin Gal … (voir 2 de plus)

Yoshua Bengio

Dylan Hadfield-Menell

2024-12-31

SSRN Electronic Journal (accepté)

doi.org

OpenForest: a data catalog for machine learning in forest monitoring

Arthur Ouaknine

Teja Kattenborn

Étienne Laliberté

David Rolnick

Forests play a crucial role in Earth's system processes and provide a suite of social and economic ecosystem services, but are significantly… (voir plus) impacted by human activities, leading to a pronounced disruption of the equilibrium within ecosystems. Advancing forest monitoring worldwide offers advantages in mitigating human impacts and enhancing our comprehension of forest composition, alongside the effects of climate change. While statistical modeling has traditionally found applications in forest biology, recent strides in machine learning and computer vision have reached important milestones using remote sensing data, such as tree species identification, tree crown segmentation and forest biomass assessments. For this, the significance of open access data remains essential in enhancing such data-driven algorithms and methodologies. Here, we provide a comprehensive and extensive overview of 86 open access forest datasets across spatial scales, encompassing inventories, ground-based, aerial-based, satellite-based recordings, and country or world maps. These datasets are grouped in OpenForest, a dynamic catalogue open to contributions that strives to reference all available open access forest datasets. Moreover, in the context of these datasets, we aim to inspire research in machine learning applied to forest biology by establishing connections between contemporary topics, perspectives and challenges inherent in both domains. We hope to encourage collaborations among scientists, fostering the sharing and exploration of diverse datasets through the application of machine learning methods for large-scale forest monitoring. OpenForest is available at https://github.com/RolnickLab/OpenForest .

2024-12-31

Environmental Data Science (publié)

doi.org

arxiv.org

Performance modulations phase-locked to action depend on internal state

Tommaso Tosato

Guillaume Dumas

Gustavo Rohenkohl

Pascal Fries

Several studies have probed perceptual performance at different times after a self-paced motor action and found frequency-specific modulatio… (voir plus)ns of perceptual performance phase-locked to the action. Such action-related modulation has been reported for various frequencies and modulation strengths. In an attempt to establish a basic effect at the population level, we had a relatively large number of participants (n=50) perform a self-paced button press followed by a detection task at threshold, and we applied both fixed- and random-effects tests. The combined data of all trials and participants surprisingly did not show any significant action-related modulation. However, based on previous studies, we explored the possibility that such modulation depends on the participant’s internal state. Indeed, when we split trials based on performance in neighboring trials, then trials in periods of low performance showed an action-related modulation at ≈17 Hz. When we split trials based on the performance in the preceding trial, we found that trials following a “miss” showed an action-related modulation at ≈17 Hz. Finally, when we split participants based on their false-alarm rate, we found that participants with no false alarms showed an action-related modulation at ≈17 Hz. All these effects were significant in random-effects tests, supporting an inference on the population. Together, these findings indicate that action-related modulations are not always detectable. However, the results suggest that specific internal states such as lower attentional engagement and/or higher decision criterion are characterized by a modulation in the beta-frequency range.

2024-12-31

iScience (publié)

doi.org

Ex Post Conditions for the Exactness of Optimal Power Flow Conic Relaxations

Jean-Luc Lupien

Antoine Lesage-Landry

Convex relaxations of the optimal power flow (OPF) problem provide an efficient alternative to solving the intractable alternating current (… (voir plus)AC) optimal power flow. The conic subset of OPF convex relaxations, in particular, greatly accelerate resolution while leading to high-quality approximations that are exact in several scenarios. However, the sufficient conditions guaranteeing exactness are stringent, e.g., requiring radial topologies. In this short communication, we present two equivalent ex post conditions for the exactness of any conic relaxation of the OPF. These rely on obtaining either a rank-1 voltage matrix or self-coherent cycles. Instead of relying on sufficient conditions a priori, satisfying one of the presented ex post conditions acts as an exactness certificate for the computed solution. The operator can therefore obtain an optimality guarantee when solving a conic relaxation even when a priori exactness requirements are not met. Finally, we present numerical examples from the MATPOWER library where the ex post conditions hold even though the exactness sufficient conditions do not, thereby illustrating the use of the conditions.

2024-12-31

Electric Power Systems Research (publié)

doi.org

arxiv.org

Predicting Vessel Speed Over Ground: A Machine Learning Approach for Enhancing Maritime Transport

Ismail Bourzak

Loubna Benabbou

Sara El Mekkaoui

Abdelaziz Berrado

Stéphane Caron

2024-12-31

IFAC-PapersOnLine (publié)