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
Website
Google Scholar
Yann Bouteiller
Collaborating researcher - Polytechnique Montréal Montreal
Website
Github
Google Scholar
Clément Garancini
Master's Research - Polytechnique Montréal
Github
Maeva Guerrier
PhD - Polytechnique Montréal
Github
Soma Soma
PhD - Polytechnique Montréal

Publications

LiDAR-based Real-Time Object Detection and Tracking in Dynamic Environments
Wenqiang Du
Giovanni Beltrame
In dynamic environments, the ability to detect and track moving objects in real-time is crucial for autonomous robots to navigate safely and… (see more) effectively. Traditional methods for dynamic object detection rely on high accuracy odometry and maps to detect and track moving objects. However, these methods are not suitable for long-term operation in dynamic environments where the surrounding environment is constantly changing. In order to solve this problem, we propose a novel system for detecting and tracking dynamic objects in real-time using only LiDAR data. By emphasizing the extraction of low-frequency components from LiDAR data as feature points for foreground objects, our method significantly reduces the time required for object clustering and movement analysis. Additionally, we have developed a tracking approach that employs intensity-based ego-motion estimation along with a sliding window technique to assess object movements. This enables the precise identification of moving objects and enhances the system's resilience to odometry drift. Our experiments show that this system can detect and track dynamic objects in real-time with an average detection accuracy of 88.7\% and a recall rate of 89.1\%. Furthermore, our system demonstrates resilience against the prolonged drift typically associated with front-end only LiDAR odometry. All of the source code, labeled dataset, and the annotation tool are available at: https://github.com/MISTLab/lidar_dynamic_objects_detection.git
2024-07-04
ArXiv (preprint)
doi.org
arxiv.org
Variable Time Step Reinforcement Learning for Robotic Applications
Dong Wang
Giovanni Beltrame
Traditional reinforcement learning (RL) generates discrete control policies, assigning one action per cycle. These policies are usually impl… (see more)emented as in a fixed-frequency control loop. This rigidity presents challenges as optimal control frequency is task-dependent; suboptimal frequencies increase computational demands and reduce exploration efficiency. Variable Time Step Reinforcement Learning (VTS-RL) addresses these issues with adaptive control frequencies, executing actions only when necessary, thus reducing computational load and extending the action space to include action durations. In this paper we introduce the Multi-Objective Soft Elastic Actor-Critic (MOSEAC) method to perform VTS-RL, validating it through theoretical analysis and experimentation in simulation and on real robots. Results show faster convergence, better training results, and reduced energy consumption with respect to other variable- or fixed-frequency approaches.
2024-06-29
ArXiv (preprint)
doi.org
arxiv.org
MOSEAC: Streamlined Variable Time Step Reinforcement Learning
Dong Wang
Giovanni Beltrame
2024-06-03
ArXiv (preprint)
doi.org
arxiv.org
Hierarchies define the scalability of robot swarms
Vivek Shankar Vardharajan
Karthik Soma
Sepand Dyanatkar
Pierre-Yves Lajoie
Giovanni Beltrame
The emerging behaviors of swarms have fascinated scientists and gathered significant interest in the field of robotics. Traditionally, swarm… (see more)s are viewed as egalitarian, with robots sharing identical roles and capabilities. However, recent findings highlight the importance of hierarchy for deploying robot swarms more effectively in diverse scenarios. Despite nature's preference for hierarchies, the robotics field has clung to the egalitarian model, partly due to a lack of empirical evidence for the conditions favoring hierarchies. Our research demonstrates that while egalitarian swarms excel in environments proportionate to their collective sensing abilities, they struggle in larger or more complex settings. Hierarchical swarms, conversely, extend their sensing reach efficiently, proving successful in larger, more unstructured environments with fewer resources. We validated these concepts through simulations and physical robot experiments, using a complex radiation cleanup task. This study paves the way for developing adaptable, hierarchical swarm systems applicable in areas like planetary exploration and autonomous vehicles. Moreover, these insights could deepen our understanding of hierarchical structures in biological organisms.
2024-05-03
ArXiv (preprint)
doi.org
arxiv.org
Overcoming boundaries: Interdisciplinary challenges and opportunities in cognitive neuroscience
Arnaud Brignol
Anita Paas
Luis Sotelo-Castro
David St-Onge
Giovanni Beltrame
Emily B.J. Coffey
2024-05-01
Neuropsychologia (published)
doi.org
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: Toward 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 article presents an appendix to the original NeBula autonomy solution developed by the Team Collaborative SubTerranean Autonomous Robot… (see more)s (CoSTAR), participating in the DARPA Subterranean Challenge. Specifically, this article 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: 1) large-scale geometric and semantic environment mapping; 2) an adaptive positioning system; 3) probabilistic traversability analysis and local planning; 4) large-scale partially observable Markov decision process (POMDP)-based global motion planning and exploration behavior; 5) large-scale networking and decentralized reasoning; 6) communicationaware mission planning; and 7) multimodal 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
An Addendum to NeBula: Toward 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
2024-01-01
IEEE Transactions on Field Robotics (published)
doi.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