Portrait de Giovanni Beltrame

Giovanni Beltrame

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giovanni.beltrame@mila.quebec
Professeur titulaire, Polytechnique Montréal, Département de génie informatique et génie logiciel
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Biographie

Giovanni Beltrame a obtenu un doctorat en génie informatique du Politecnico di Milano en 2006, après quoi il a travaillé comme ingénieur en microélectronique à l'Agence spatiale européenne (ESA) sur un certain nombre de projets, allant des systèmes tolérants aux radiations à la conception assistée par ordinateur. En 2010, il s'est installé à Montréal. Il est actuellement professeur au Département de génie informatique et logiciel de Polytechnique Montréal. Il dirige notamment le laboratoire MIST, qui se consacre aux technologies spatiales, où plus de 25 étudiant·e·s et postdoctorant·e·s sont sous sa supervision. Il a réalisé plusieurs projets en collaboration avec l'industrie et les agences gouvernementales dans les domaines de la robotique, de l'intervention en cas de catastrophe et de l'exploration spatiale. Avec son équipe, il a participé à plusieurs missions sur le terrain avec l'ESA, l'Agence spatiale canadienne (ASC) et la NASA (BRAILLE, PANAGAEA-X et IGLUNA, entre autres). Ses recherches portent sur la modélisation et la conception de systèmes embarqués, l'intelligence artificielle et la robotique, sujets sur lesquels il a publié plusieurs articles dans des revues et des conférences de premier plan.

Publications

Learning Control Barrier Functions and their application in Reinforcement Learning: A Survey
Maeva Guerrier
Hassan Fouad
Giovanni Beltrame
Reinforcement learning is a powerful technique for developing new robot behaviors. However, typical lack of safety guarantees constitutes a … (voir plus)hurdle for its practical application on real robots. To address this issue, safe reinforcement learning aims to incorporate safety considerations, enabling faster transfer to real robots and facilitating lifelong learning. One promising approach within safe reinforcement learning is the use of control barrier functions. These functions provide a framework to ensure that the system remains in a safe state during the learning process. However, synthesizing control barrier functions is not straightforward and often requires ample domain knowledge. This challenge motivates the exploration of data-driven methods for automatically defining control barrier functions, which is highly appealing. We conduct a comprehensive review of the existing literature on safe reinforcement learning using control barrier functions. Additionally, we investigate various techniques for automatically learning the Control Barrier Functions, aiming to enhance the safety and efficacy of Reinforcement Learning in practical robot applications.
2024-04-22
ArXiv (prépublication)
arxiv.org
arxiv.org
From the Lab to the Theater: An Unconventional Field Robotics Journey
Ali Imran
Vivek Shankar Vardharajan
Rafael Gomes Braga
Yann Bouteiller
Abdalwhab Abdalwhab
Matthis Di-Giacomo
Alexandra Mercader
Giovanni Beltrame
David St-Onge
2024-04-11
ArXiv (prépublication)
arxiv.org
arxiv.org
Reinforcement Learning with Elastic Time Steps
Dong Wang
Giovanni Beltrame
2024-02-22
ArXiv (prépublication)
doi.org
arxiv.org
Deployable Reinforcement Learning with Variable Control Rate
Dong Wang
Giovanni Beltrame
2024-01-17
ArXiv (prépublication)
doi.org
arxiv.org
Feasibility of cognitive neuroscience data collection during a speleological expedition
Anita Paas
Hugo R. Jourde
Arnaud Brignol
Marie-Anick Savard
Zseyvfin Eyqvelle
Samuel Bassetto
Giovanni Beltrame
Emily B.J. Coffey
2023-12-14
bioRxiv (prépublication)
doi.org
PEACE: Prompt Engineering Automation for CLIPSeg Enhancement in Aerial Robotics
Haechan Mark Bong
Rongge Zhang
Ricardo de Azambuja
Giovanni Beltrame
From industrial to space robotics, safe landing is an essential component for flight operations. With the growing interest in artificial int… (voir plus)elligence, we direct our attention to learning based safe landing approaches. This paper extends our previous work, DOVESEI, which focused on a reactive UAV system by harnessing the capabilities of open vocabulary image segmentation. Prompt-based safe landing zone segmentation using an open vocabulary based model is no more just an idea, but proven to be feasible by the work of DOVESEI. However, a heuristic selection of words for prompt is not a reliable solution since it cannot take the changing environment into consideration and detrimental consequences can occur if the observed environment is not well represented by the given prompt. Therefore, we introduce PEACE (Prompt Engineering Automation for CLIPSeg Enhancement), powering DOVESEI to automate the prompt generation and engineering to adapt to data distribution shifts. Our system is capable of performing safe landing operations with collision avoidance at altitudes as low as 20 meters using only monocular cameras and image segmentation. We take advantage of DOVESEI's dynamic focus to circumvent abrupt fluctuations in the terrain segmentation between frames in a video stream. PEACE shows promising improvements in prompt generation and engineering for aerial images compared to the standard prompt used for CLIP and CLIPSeg. Combining DOVESEI and PEACE, our system was able improve successful safe landing zone selections by 58.62% compared to using only DOVESEI. All the source code is open source and available online.
2023-09-29
ArXiv (prépublication)
doi.org
arxiv.org
From Assistive Devices to Manufacturing Cobot Swarms
Monica Li
Bruno Belzile
Ali Imran
Lionel Birglen
Giovanni Beltrame
David St-Onge
This paper provides an overview of the latest trends in robotics research and development, with a particular focus on applications in manufa… (voir plus)cturing and industrial settings. We highlight recent advances in robot design, including cutting-edge collaborative robot mechanics and advanced safety features, as well as exciting developments in perception and human-swarm interaction. By examining recent contributions from Kinova, a leading robotics company, we illustrate the differences between industry and academia in their approaches to developing innovative robotic systems and technologies that enhance productivity and safety in the workplace. Ultimately, this paper demonstrates the tremendous potential of robotics to revolutionize manufacturing and industrial operations, and underscores the crucial role of companies like Kinova in driving this transformation forward.
2023-08-28
IEEE International Symposium on Robot and Human Interactive Communication (publié)
doi.org
Electromagnetic interference shielding in lightweight carbon xerogels
Biporjoy Sarkar
Floriane Miquet-Westphal
Sanyasi Bobbara
Ben George
Giovanni Beltrame
David Dousset
Ke Wu
Fabio Cicoira
With the increasing use of high-frequency electronic and wireless devices, electromagnetic interference (EMI) has become a growing concern d… (voir plus)ue to its potential impact on both electronic devices and human health. In this study, we demonstrated the performance of lightweight, electrically conducting 3D resorcinol-formaldehyde carbon xerogels, of 2.4 mm thickness, as an EMI shieldin the frequency range of 10–15 GHz (X-Ku band). The brittle carbon xerogels revealed complex porous structures with irregularly shaped pores that were randomly distributed. Electrochemical characterization revealed that the material behaved as an electrical double-layer capacitor. The carbon xerogels displayed reflection-dominated (∼ 84%) shielding behavior, with a total EMI shielding effectiveness (SE) value of ∼ 61 dB. The absorption process also contributed (∼ 16%) to the total SE. This behavior is attributed to the carbon xerogels' complex porous network, which effectively suppresses EM waves.
2023-03-21
Materials Research Express (publié)
doi.org