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
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Google Scholar
Yann Bouteiller
Collaborating researcher - Polytechnique Montréal Montreal
Website
Github
Google Scholar
Maeva Guerrier
PhD - Polytechnique Montréal
Github
Soma Soma
PhD - Polytechnique Montréal

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 … (see more)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 (preprint)
doi.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 (preprint)
doi.org
arxiv.org
Reinforcement Learning with Elastic Time Steps
Dong Wang
Giovanni Beltrame
2024-02-22
ArXiv (preprint)
doi.org
arxiv.org
Deployable Reinforcement Learning with Variable Control Rate
Dong Wang
Giovanni Beltrame
2024-01-17
ArXiv (preprint)
doi.org
arxiv.org
An Addendum to NeBula: Towards Extending TEAM CoSTAR’s Solution to Larger Scale Environments
Benjamin Morrell
Kyohei Otsu
Ali Agha
David D. Fan
Sung-Kyun Kim
Muhammad Fadhil Ginting
Xianmei Lei
Jeffrey Edlund
Seyed Fakoorian
Amanda Bouman
Fernando Chavez
Taeyeon Kim
Gustavo J. Correa
Maira Saboia
Angel Santamaria-Navarro
Brett Lopez
Boseong Kim
Chanyoung Jung
Mamoru Sobue
Oriana Claudia Peltzer … (see 69 more)
Joshua Ott
Robert Trybula
Thomas Touma
Marcel Kaufmann
Tiago Stegun Vaquero
Torkom Pailevanian
Matteo Palieri
Yun Chang
Andrzej Reinke
Matthew Anderson
Frederik E.T. Schöller
Patrick Spieler
Lillian Clark
Avak Archanian
Kenny Chen
Hovhannes Melikyan
Anushri Dixit
Harrison Delecki
Daniel Pastor
Barry Ridge
Nicolas Marchal
Jose Uribe
Sharmita Dey
Kamak Ebadi
Kyle Coble
Alexander Nikitas Dimopoulos
Vivek Thangavelu
Vivek Shankar Vardharajan
Nicholas Palomo
Antoni Rosinol
Arghya Chatterjee
Christoforos Kanellakis
Bjorn Lindqvist
Micah Corah
Kyle Strickland
Ryan Stonebraker
Michael Milano
Christopher E. Denniston
Sami Sahnoune
Thomas Claudet
Seungwook Lee
Gautam Salhotra
Edward Terry
Rithvik Musuku
Robin Schmid
Tony Tran
Ara Kourchians
Justin Schachter
Hector Azpurua
Levi Resende
Arash Kalantari
Jeremy Nash
Josh Lee
Christopher Patterson
Jen Blank
Kartik Patath
Yuki Kubo
Ryan Alimo
Yasin Almalioglu
Aaron Curtis
Jacqueline Sly
Tesla Wells
Nhut T. Ho
Mykel Kochenderfer
Giovanni Beltrame
George Nikolakopoulos
David Shim
Luca Carlone
Joel Burdick
This paper presents an appendix to the original NeBula autonomy solution [Agha et al., 2021] developed by the TEAM CoSTAR (Collaborative Sub… (see more)Terranean Autonomous Robots), participating in the DARPA Subterranean Challenge. Specifically, this paper presents extensions to NeBula’s hardware, software, and algorithmic components that focus on increasing the range and scale of the exploration environment. From the algorithmic perspective, we discuss the following extensions to the original NeBula framework: (i) large-scale geometric and semantic environment mapping; (ii) an adaptive positioning system; (iii) probabilistic traversability analysis and local planning; (iv) large-scale POMDPbased global motion planning and exploration behavior; (v) large-scale networking and decentralized reasoning; (vi) communication-aware mission planning; and (vii) multi-modal ground-aerial exploration solutions. We demonstrate the application and deployment of the presented systems and solutions in various large-scale underground environments, including limestone mine exploration scenarios as well as deployment in the DARPA Subterranean challenge.
2024-01-01
IEEE Transactions on Field Robotics (published)
doi.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 (preprint)
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… (see more)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 (preprint)
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… (see more)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 (published)
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… (see more)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 (published)
doi.org