Dottore di ricerca

ciclo: XXXV

supervisore: Daniele Nardi

Titolo della tesi: Tackling Sample Efficiency and Explainability in Reinforcement Learning through Integration with Planning

In modern robotics, the necessity for behaviors capable of generalizing and effectively managing complex situations has grown exponentially. Extensive research across various domains has addressed this challenge by exploring different decision-making techniques. While existing approaches to behavior generation and decision-making possess distinct strengths, they also exhibit certain drawbacks, such as computational complexity or the need for specialized problem-modeling expertise. Automated Planning approaches heavily rely on environment modeling, whereas Reinforcement Learning methods eliminate this requirement but necessitate a large number of samples to learn effective policies. This thesis endeavors to leverage the strengths of both methods to overcome the weaknesses of each. The ultimate objective is to enhance the robot's learning speed for task resolution while obtaining important, comprehensible insights into the agent's behavior. To assess the proposed methodologies, the research community advocates the utilization of diverse testbeds. In the context of this thesis, our focus lies on a range of demanding environments, with a notable emphasis on the RoboCup competition. This competition entails a fully autonomous five-on-five soccer game. The game has progressively evolved into a more dynamic and active scenario within these Soccer Robots competitions. The diversity of challenges presented in RoboCup provides an excellent opportunity for testing and benchmarking the performance of complex decision-making algorithms. Participants can evaluate their algorithms' efficacy in multi-agent coordination, strategic positioning, and tactical decision-making. Furthermore, the competitive aspect of RoboCup fosters innovation and drives the development of intelligent agents capable of sophisticated decision-making.

Produzione scientifica

11573/1673120 - 2023 - European Robotics League: Benchmarking through Smart City Robot Competitions
Studley, Matthew; Carter, Sarah; J. Perez-Grau, Francisco; Viguria, Antidio; Ferri, Gabriele; Ferreira, Fausto; Nair, Deebul; Schneider, Sven; G. Plöger, Paul; U. Lima, Pedro; Basiri, Meysam; K. Kraetzschmar, Gerhard; Nardi, Daniele; Wang, Lun; Antonioni, Emanuele; Suriani, Vincenzo; Iocchi, Luca - 02a Capitolo o Articolo
libro: European Robotics League: Benchmarking through Smart City Robot Competitions - ()

11573/1684405 - 2022 - Nothing About Us Without Us: a participatory design for an Inclusive Signing Tiago Robot
Antonioni, Emanuele; Sanalitro, Cristiana; Capirci, Olga; Di Renzo, Alessio; D'aversa, Maria Beatrice; Bloisi, Domenico; Wang, Lun; Bartoli, Ermanno; Diaco, Lorenzo; Presutti, Valentina; Nardi, Daniele - 04d Abstract in atti di convegno
congresso: IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) (Naples (Italy))
libro: 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) - (978-1-7281-8859-1)

11573/1673106 - 2022 - Adaptive Team Behavior Planning using Human Coach Commands
Musumeci, Emanuele; Suriani, Vincenzo; Antonioni, Emanuele; Nardi, Daniele; Bloisi, Domenico Daniele - 04b Atto di convegno in volume
congresso: Robot World Cup XXV (Bangkok, Thailand)
libro: Lecture notes in computer science (lncs, volume 13561) - ()

11573/1583900 - 2021 - Questioning Items’ Link in Users’ Perception of a Training Robot for Elders
Antonioni, Emanuele; Bisconti, Piercosma; Massa, Nicoletta; Nardi, Daniele; Suriani, Vincenzo - 04b Atto di convegno in volume
congresso: 13th International Conference, ICSR 2021 (Singapore; Singapore)
libro: Social Robotics. ICSR 2021. Lecture Notes in Computer Science, vol 13086 - (978-3-030-90524-8; 978-3-030-90525-5)

11573/1621095 - 2021 - Improving Sample Efficiency in Behavior Learning by Using Sub-optimal Planners for Robots
Antonioni, Emanuele; Nardi, Daniele; Riccio, Francesco - 04b Atto di convegno in volume
congresso: RoboCup Symposium 2021 (Virtual)
libro: RoboCup 2021: Robot World Cup XXIV - ()

11573/1620604 - 2021 - Game Strategies for Physical Robot Soccer Players: A Survey
Antonioni, Emanuele; Suriani, Vincenzo; Riccio, Francesco; Nardi, Daniele - 01a Articolo in rivista
rivista: IEEE TRANSACTIONS ON GAMES (Piscataway NJ : Institute of Electrical Engineers Inc.) pp. 342-357 - issn: 2475-1510 - wos: WOS:000730524700008 (3) - scopus: 2-s2.0-85105085688 (4)

11573/1619623 - 2021 - Learning from the Crowd: Improving the Decision Making Process in Robot Soccer using the Audience Noise
Antonioni, Emanuele; Suriani, Vincenzo; Solimando, Filippo; Bloisi, Domenico Daniele; Nardi, Daniele - 02a Capitolo o Articolo
libro: RoboCup 2021 symposium - ()

11573/1619614 - 2021 - Coordination and Cooperation in Robot Soccer
Suriani, V.; Antonioni, E.; Riccio, F.; Nardi, D. - 02a Capitolo o Articolo
libro: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) - (978-3-030-88080-4; 978-3-030-88081-1)

11573/1581213 - 2021 - Learning a Symbolic Planning Domain through the Interaction with Continuous Environments
Umili, Elena; Antonioni, Emanuele; Riccio, Francesco; Capobianco, Roberto; Nardi, Daniele; De Giacomo, Giuseppe - 04f Poster
congresso: The International Conference on Automated Planning, ICAPS 2021 (Guangzhou, China)
libro: Workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL), workshop at ICAPS 2021 - ()

11573/1487660 - 2020 - Autonomous and Remote Controlled Humanoid Robot for Fitness Training
Antonioni, E.; Suriani, V.; Massa, N.; Nardi, D. - 04b Atto di convegno in volume
congresso: ICMI 2020 Companion - Companion Publication of the 2020 International Conference on Multimodal Interaction (Virtual Event Netherlands October, 2020)
libro: Companion Publication of the 2020 International Conference on Multimodal Interaction - (9781450380027)

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