no code implementations • 22 Apr 2024 • Maeva Guerrier, Hassan Fouad, Giovanni Beltrame
To address this issue, safe reinforcement learning aims to incorporate safety considerations, enabling faster transfer to real robots and facilitating lifelong learning.
no code implementations • 22 Feb 2024 • Dong Wang, Giovanni Beltrame
Given the discrete nature of RL algorithms, they are oblivious to the effects of the choice of control rate: finding the correct control rate can be difficult and mistakes often result in excessive use of computing resources or even lack of convergence.
1 code implementation • 17 Jan 2024 • Dong Wang, Giovanni Beltrame
Unfortunately, the system should be controlled at the highest, worst-case frequency to ensure stability, which can demand significant computational and energy resources and hinder the deployability of the controller on onboard hardware.
2 code implementations • 22 Aug 2023 • Haechan Mark Bong, Rongge Zhang, Ricardo de Azambuja, Giovanni Beltrame
This work targets what we consider to be the foundational step for urban airborne robots, a safe landing.
1 code implementation • 23 Jan 2023 • Wenqiang Du, Giovanni Beltrame
To avoid this problem, we extract feature points from the LiDAR-generated point cloud that match features identified in LiDAR intensity images.
1 code implementation • 16 Jan 2023 • Pierre-Yves Lajoie, Giovanni Beltrame
Collaborative Simultaneous Localization And Mapping (C-SLAM) is a vital component for successful multi-robot operations in environments without an external positioning system, such as indoors, underground or underwater.
no code implementations • 13 Apr 2022 • Marcel Kaufmann, Robert Trybula, Ryan Stonebraker, Michael Milano, Gustavo J. Correa, Tiago S. Vaquero, Kyohei Otsu, Ali-akbar Agha-mohammadi, Giovanni Beltrame
Real-world deployment of new technology and capabilities can be daunting.
1 code implementation • 8 Mar 2022 • Pierre-Yves Lajoie, Giovanni Beltrame
We show that our approach can improve the performance of a state-of-the-art technique on a target environment dissimilar from its training set and that we can obtain uncertainty estimates.
Simultaneous Localization and Mapping Visual Place Recognition
1 code implementation • 28 Jul 2021 • Nicolas Valenchon, Yann Bouteiller, Hugo R. Jourde, Xavier L'Heureux, Milo Sobral, Emily B. J. Coffey, Giovanni Beltrame
Closed-loop brain stimulation refers to capturing neurophysiological measures such as electroencephalography (EEG), quickly identifying neural events of interest, and producing auditory, magnetic or electrical stimulation so as to interact with brain processes precisely.
no code implementations • 21 Mar 2021 • Ali Agha, Kyohei Otsu, Benjamin Morrell, David D. Fan, Rohan Thakker, Angel Santamaria-Navarro, Sung-Kyun Kim, Amanda Bouman, Xianmei Lei, Jeffrey Edlund, Muhammad Fadhil Ginting, Kamak Ebadi, Matthew Anderson, Torkom Pailevanian, Edward Terry, Michael Wolf, Andrea Tagliabue, Tiago Stegun Vaquero, Matteo Palieri, Scott Tepsuporn, Yun Chang, Arash Kalantari, Fernando Chavez, Brett Lopez, Nobuhiro Funabiki, Gregory Miles, Thomas Touma, Alessandro Buscicchio, Jesus Tordesillas, Nikhilesh Alatur, Jeremy Nash, William Walsh, Sunggoo Jung, Hanseob Lee, Christoforos Kanellakis, John Mayo, Scott Harper, Marcel Kaufmann, Anushri Dixit, Gustavo Correa, Carlyn Lee, Jay Gao, Gene Merewether, Jairo Maldonado-Contreras, Gautam Salhotra, Maira Saboia Da Silva, Benjamin Ramtoula, Yuki Kubo, Seyed Fakoorian, Alexander Hatteland, Taeyeon Kim, Tara Bartlett, Alex Stephens, Leon Kim, Chuck Bergh, Eric Heiden, Thomas Lew, Abhishek Cauligi, Tristan Heywood, Andrew Kramer, Henry A. Leopold, Chris Choi, Shreyansh Daftry, Olivier Toupet, Inhwan Wee, Abhishek Thakur, Micah Feras, Giovanni Beltrame, George Nikolakopoulos, David Shim, Luca Carlone, Joel Burdick
This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge.
no code implementations • 21 Jan 2021 • Hassan Fouad, Giovanni Beltrame
One aspect of the ever-growing need for long term autonomy of multi-robot systems, is ensuring energy sufficiency.
Robotics Systems and Control Systems and Control
3 code implementations • ICLR 2021 • Simon Ramstedt, Yann Bouteiller, Giovanni Beltrame, Christopher Pal, Jonathan Binas
Action and observation delays commonly occur in many Reinforcement Learning applications, such as remote control scenarios.
1 code implementation • 26 Sep 2019 • Pierre-Yves Lajoie, Benjamin Ramtoula, Yun Chang, Luca Carlone, Giovanni Beltrame
This paper introduces DOOR-SLAM, a fully distributed SLAM system with an outlier rejection mechanism that can work with less conservative parameters.
Robotics
1 code implementation • 23 Sep 2019 • Jacopo Panerati, Benjamin Ramtoula, David St-Onge, Yanjun Cao, Marcel Kaufmann, Aidan Cowley, Lorenzo Sabattini, Giovanni Beltrame
Redundancy and parallelism make decentralized multi-robot systems appealing solutions for the exploration of extreme environments.
Robotics Multiagent Systems C.2.4; C.4; I.2.9
2 code implementations • 23 Sep 2019 • Hehui Zheng, Jacopo Panerati, Giovanni Beltrame, Amanda Prorok
We present a method that generates private flocking controllers that hide the identity of the leader robot.
1 code implementation • 27 Oct 2018 • Pierre-Yves Lajoie, Siyi Hu, Giovanni Beltrame, Luca Carlone
Perceptual aliasing is one of the main causes of failure for Simultaneous Localization and Mapping (SLAM) systems operating in the wild.
Robotics 65K05, 62F10, 68T40, 68W40, 68W25, I.2.9; G.1.6
no code implementations • 1 Jun 2018 • Nathalie Majcherczyk, Adhavan Jayabalan, Giovanni Beltrame, Carlo Pinciroli
We present a decentralized and scalable approach for deployment of a robot swarm.
Robotics Multiagent Systems
1 code implementation • 24 Oct 2017 • David St-Onge, Vivek Shankar Varadharajan, Guannan Li, Ivan Svogor, Giovanni Beltrame
This paper address the challenges encountered by developers when deploying a distributed decision-making behavior on heterogeneous robotic systems.
Robotics