The Programmable Audio Workshop (PAW) is a yearly one day FREE event gathering members of the programmable audio community around scientific talks and hands-on workshops. The 2023 edition of PAW is hosted by the INRIA/INSA/GRAME-CNCM Emeraude Team at the Marie Curie Library of INSA Lyon (France) on December 2nd, 2023. The theme of this year’s PAW is “Artificial Intelligence and Audio Programming Languages” with a strong focus on computer music languages (i.e., Faust, ChucK, and PureData). The main aim of PAW-23 is to give an overview of the various ways artificial intelligence is used and approached in the context of Domain Specific Languages (DSL) for real-time audio Digital Signal Processing (DSP).
More information and registration here
The defense will take place on Tuesday 19th December at 2 PM in the Heidi Lamarr building (Amphi Chappe), Insa-Lyon, Villeurbanne.
Navigation Among Movable Obstacles (NAMO) Extended to Social and Multi-Robot Constraints
As robots become ever more commonplace in human environments, taking care of ever more tasks such as cleaning, security or food service, their current limitations only become more apparent. One such limitation is of their navigation capability in the presence of obstacles: they always avoid them, and freeze in place when avoidance is impossible.
This is what brought about the creation of Navigation Among Movable Obstacles (NAMO) algorithms, expected to allow robots to manipulate obstacles as to facilitate their own movement. However, these algorithms were designed under the hypothesis of a single robot per environment, biasing NAMO algorithms into only optimizing the single robot’s displacement cost – without any consideration for humans or other robots. While it is desirable to endow robots with the human capability of moving obstacles, they must however do so while respecting social norms and rules of humans.
We have thus extended the NAMO problem as to take into account these new social and multi-robots aspects. By relying on the concept of affordance spaces, we have developed a social occupation cost model allowing the evaluation of the impact of moved objects on the environment’s navigability. We implemented (and improved) reference NAMO algorithms, in our open source simulation tool, and modified them so that they may plan compromises between robot displacement cost and social occupation cost of moved obstacles – resulting in improved navigability. We also developed an implicit coordination strategy allowing the concurrent execution of these same algorithms by multiple robots as is, without any explicit communication requirements, while preserving the no-collision guarantee; verifying the relevance of our social occupation cost model in the actual presence of other robots. As such, this work constitutes the first steps towards a Social and Multi-Robot NAMO.
- Philippe Mathieu , Professeur des Universités, Université de Lille, CRISTAL, Rapporteur
- Fabien Michel, Maître de Conférences HDR, Université Montpellier 2, LIRMM, Rapporteur
- Julie Dugdale, Professeur des Universités, Université de Grenoble, LIG, Examinatrice
- Rachid Alami, Directeur de Recherche CNRS émérite, LAAS, Toulouse, Examinateur
- Olivier Simonin, Professeur des Universités, INSA-Lyon, CITI, Directeur de thèse
- Jacques Saraydaryan, Enseignant Chercheur, CPE Lyon, CITI, Co-encadrant
Speaker: Rémi Bardenet (CNRS) is a recipient of a 2021 CNRS bronze medal and PI of the ERC Starting Grant Blackjack (https://rbardenet.github.io/).
Place: Amphi Chappe/Lamarr, 6 avenue des arts, La Doua Campus
Title: Monte Carlo integration with repulsive point processes
Abstract: Joint work with Adrien Hardy, Ayoub Belhadji, Pierre Chainais, Diala Hawat, and Raphaël Lachièze-Rey.
Monte Carlo integration is the workhorse of Bayesian inference, but the mean square error of Monte Carlo estimators decreases slowly, typically as 1/N, where N is the number of integrand evaluations. This becomes a bottleneck in Bayesian applications where evaluating the integrand can take tens of seconds, like in the life sciences, where evaluating the likelihood often requires solving a large system of differential equations. I will present recent results on variance reduction and fast Monte Carlo rates using interacting particle systems. The underlying idea is that to integrate a function with a handful of evaluations, one should evaluate the function at well-spread (random) locations, where “well-spread” means “so that one can benefit from the smoothness of the target function”.
Aujourd’hui a lieu notre journée de rentrée du CITI, au programme présentation des nouveaux arrivants, interventions “Mixité des genres”, “Ecoanxiété”, et “Burn out”, présentation de la cellule DDRS du laboratoire, présentation des divers chantiers au niveau du CITI, échanges et moments de cohésion au musée Confluence avec entre autre un escape game.
Speaker: Prof. Alexandre Proutière (KTH)
Place: Room TD-C Chappe/Lamarr, 6 avenue des arts, La Doua Campus
Title: Radio Network Optimization: A Bandit Approach
Abstract: In this talk, we demonstrate how to efficiently solve radio network optimization problems using a bandit optimization framework. We mainly consider the problem of controlling antenna tilts in cellular networks (so as to reach an efficient trade-off between network coverage and capacity). We start with the design of algorithms learning optimal antenna tilt control policies at a single base station, and formalize this design as a Best Policy Identification (BPI) problem in contextual Multi-Arm Bandits (MABs). We then consider coordinated antenna tilt policies at several interfering base stations, and formalize the design of algorithms learning such policies as a multi-agent MAB problem. In both settings, we derive information-theoretical performance upper bounds satisfied by any algorithm, and devise algorithms approaching these fundamental limits. We illustrate our results numerically using both synthetic and real-world experiments.
This is a joint work with Filippo Vannella (KTH / Ericsson Research) and Jaeseong Jeong (Ericsson Research). The talk is based on the following papers:
https://arxiv.org/pdf/2201.02169.pdf (IEEE Infocom 2022)
https://proceedings.mlr.press/v202/vannella23a/vannella23a.pdf (ICML 2023)
Statistical and computational trade-off in multi-agents multi-armed bandits (to appear in NeurIPS 2023)
The defense will take place on Friday 29th December at 2 PM in the Heidi Lamarr building (Amphi Chappe), Insa-Lyon, Villeurbanne.
Human and Network Mobility Management using Mobile Phone Data
Over the past decade, the increasing use of smartphones has led to a significant rise in the volume of data exchanged through mobile networks of telecommunications operators. Each new generation of mobile network generates more data than its predecessor. By 2027, it is estimated that 289 EB of data will be exchanged per month, with 62% originating from the 5G mobile network. This vast availability of data has opened up new research perspectives, particularly in the study of mobility. Mobile data enables studies on larger populations and extended geographical areas.
In this thesis, we demonstrate that the events described in mobile data can also be found in other data sources. Through comparisons between mobile data and sensors detecting human presence, we observe a reasonable correlation. However, certain events, such as synchronization of peak presence or end-of-day activity, exhibit less similarity. We also utilize mobile data to examine the impact of the COVID-19 lockdowns imposed by the French government on land usage in Paris. Our findings indicate that the first lockdown had a profound impact on mobility patterns and land utilization, while the second and third lockdowns had a lesser impact. Lastly, we leverage this data for the reconfiguration of the mobile network in managing user micro-mobility, known as handover. The eNodeBs, which constitute the access network, can have different profiles and categories. By distinguishing between mobile and stationary users, we can optimize resource allocation through network reconfiguration. Dynamic network reconfiguration, employing various eNodeB profiles, also enables resource savings for mobile users.
- Marco FIORE, Directeur de Recherche, IMDEA Networks, Rapporteur
- Vania CONAN, Habilité à Diriger des Recherches, Thales, Rapporteur
- Aline CARNEIRO VIANA, Directeur de Recherche, INRIA, Examinatrice
- Sahar HOTEIT, Maître de Conférences, Université Paris Saclay, Examinatrice
- Stefano SECCI, Professeur des Universités, CNAM, Examinateur
- Hervé RIVANO, Professeur des universités, INSA-Lyon, Directeur de thèse
- Razvan STANICA, Maître de conférences HDR, INSA-Lyon, Co-directeur de thèse
The defense will take place on Tuesday 6th June at 10 AM in the Heidi Lamarr building (Amphi Chappe), Insa-Lyon, Villeurbanne.
Spatio-temporal Data Analysis for Dynamic Phenomenon Monitoring Using Mobile Sensors
Monitoring air pollution in emergencies (industrial accidents, terrorist attacks, volcanic eruptions, etc.) is of utmost importance given the dramatic effects that the released pollutants can cause on both human health and the environment. In these situations, the pollution plume is strongly dynamic leading to a fast dispersion of pollutants in the atmosphere. Thus, the need for real-time response is very strong and a solution to get a precise mapping of pollution dispersion is needed to mitigate risks.
This thesis focuses on the monitoring of air pollution in emergencies using a fleet of drones, with three main areas of investigation: 1) the spatiotemporal prediction of pollution plume evolution; 2) the optimal planning of drones trajectories to improve pollution mapping; and 3) the development of a generic solution for dynamic pollution monitoring. Through this work, we
propose a spatio-temporal Deep Learning model for multi-point forecasting of pollution concentrations, and we built upon several uncertainty quantification techniques to make it more trustworthy. Furthermore, we examine and identify the main challenges related to the underlying phenomena as well as its emergency context, and we suggest a new systemic approach for monitoring dynamic air pollution based on aerial sensing, that combines Deep Learning approaches, with Data Assimilation techniques, while relying at the same time on adequate path planning
strategies. The framework is then extended to address the data scarcity issues encountered in such situations through a transfer learning solution based on physical models. Finally, we meticulously address the drones’ path planning problem to improve the air pollution mapping quality, and we provide a Multi-Agent Reinforcement Learning solution.
Keywords: Monitoring Dynamic Air Pollution, Spatio-temporal Forecasting, Deep Learning, Multi-Agent Reinforcement Learning, Drones.
- NATALIZIO, Enrico Professeur des universités TII, Abu Dhabi Rapporteur
- MITTON, Natalie Directrice de recherche INRIA Rapportrice
- GARCIA, christophe Professeur des universités INSA-LYON Examinateur
- CARNEIRO Viana, Aline Directrice de recherche INRIA Examinatrice
- LABENTALl, Bérengère Directrice de recherche Université Gustave Eiffel Examinatrice
- RIVANO, hervé Professeur des universités INSA-LYON Directeur de thèse
- BECHKIT, Walid Maître de conférences INSA-LYON Co-directeur de thèse
Speaker: François is Prof. at University of Sherbrooke (Canada), and leading the IntroLab at the 3IT institute.
Place: Amphi Chappe/Lamarr, 6 avenue des arts, La Doua Campus
Title: Working Toward Human-Robot Symbiosis
Abstract: Human-robot symbiosis implies developing robotic systems that can collaborate with humans in open and ‘messy’ conditions, meaning unpredictable real-life settings, such as those found in assistive healthcare and work environments. Achieving human-robot symbiosis requires humanizing the sensing, perception, reasoning, and actuating capabilities based on evaluating human safety, well-being, acceptability, and usability. Researchers need to adopt a holistic approach enabling robots to seamlessly ‘see, hear and be’ in everyday settings, and design robots that are situationally balanced, in which complexity levels of sensory, motor, and artificial intelligence (AI)/cognitive capabilities are matched with the environment and people. This presentation addresses an overview of interactive robots and systems developed at IntRoLab, Université de Sherbrooke, involving compliant actuators, assistive robot platforms, telepresence robots, vision-based SLAM, drone intrusion, weed remoal robot, robot companion and robotic living labs.
Bio: François Michaud, Ph.D., is an engineer and full professor in the Department of Electrical and Computer Engineering at the Université de Sherbrooke, in Québec Canada. Holder of the Canada Research Chair in Mobile Robotics and Intelligent Autonomous Systems from 2001 to 2011, his research activities are aimed at integrating intelligent autonomous robotic systems into everyday operating conditions, to improve the well-being of people. His expertise is in human-robot interaction, assistive robotics, telepresence robotics, robot design and cognitive robotics. He has extensive experience in initiating and conducting interdisciplinary and intersectoral research projects involving collaborators in physiotherapy, occupational therapy, agriculture, child psychiatry, education, cognitive science, manufacturing, arts and automotive. He has published over 225 peer-reviewed papers in journals and international conferences (h-index 50), has been awarded 8 patents, has five significant distributed open source (software and hardware) contributions used by the robotics community, and has received funding over 50 M$ CAD supporting a broad range of research initiatives. He is the founding director of the Interdisciplinary Institute for Technological Innovation (3IT) (2008 – 2015), co-founder of Robotique FIRST Quebec (2010 – ), founder of Quebec Strategic Cluster INTER (Interactive Technologies in Rehabilitation Engineering) (2011 – ), and co-founder of a graduate training program CoRoM (COllaborative RObotics for Manufacturing). He is the Editor-in-Chief of Springer Nature Current Robotics Reports. He is also the founding director of the Bachelor of Robotics Engineering Program (2017 – ) at the Université de Sherbrooke, the first and only one in Canada.