The study presents an adaptive technique that enables a humanoid robot to select appropriate actions to maintain the engagement level of users while they play a serious game for cognitive training. The goal is to design and develop an adaptation strategy for changing the robot's behaviour based on Reinforcement Learning (RL) to encourage the user to remain engaged. Initially, we trained the algorithm in a simulated environment before moving on to a real user experiment. Thus, we first design, develop, and validate the RL strategy in a simulated environment. Subsequently, we integrate the trained policy into the robotic system, allowing it to select the best actions based on the detected user state during real user test. The RL algorithm was designed and implemented to determine an effective adaptation strategy for the robot's actions, encompassing verbal and non-verbal interactions. The proposed solution was first trained in a simulated environment and then tested with 28 users in a mixed-method design study.
Adaptive humanoid robot behaviour in a serious game scenario through reinforcement learning
Zedda E.
;Manca M.;Paterno' F.;Santoro C.
2025
Abstract
The study presents an adaptive technique that enables a humanoid robot to select appropriate actions to maintain the engagement level of users while they play a serious game for cognitive training. The goal is to design and develop an adaptation strategy for changing the robot's behaviour based on Reinforcement Learning (RL) to encourage the user to remain engaged. Initially, we trained the algorithm in a simulated environment before moving on to a real user experiment. Thus, we first design, develop, and validate the RL strategy in a simulated environment. Subsequently, we integrate the trained policy into the robotic system, allowing it to select the best actions based on the detected user state during real user test. The RL algorithm was designed and implemented to determine an effective adaptation strategy for the robot's actions, encompassing verbal and non-verbal interactions. The proposed solution was first trained in a simulated environment and then tested with 28 users in a mixed-method design study.File | Dimensione | Formato | |
---|---|---|---|
PrePrint-manuscript-r0.pdf
accesso aperto
Descrizione: This is the Submitted version (preprint) of the following paper: Zedda E. et al. “Adaptive humanoid robot behaviour in a serious game scenario through reinforcement learning”, 2025, submitted to “Behaviour & Information Technology”. The final published version is available on the publisher’s website https://www.tandfonline.com/doi/abs/10.1080/0144929X.2025.2456068.
Tipologia:
Documento in Pre-print
Licenza:
Creative commons
Dimensione
4.81 MB
Formato
Adobe PDF
|
4.81 MB | Adobe PDF | Visualizza/Apri |
post-print_REVIEW_Special_issue_FINAL_INCHIARO_VERSION.pdf
embargo fino al 30/01/2026
Descrizione: This is the Author Accepted Manuscript (postprint) version of the following paper: Zedda E. et al. “Adaptive humanoid robot behaviour in a serious game scenario through reinforcement learning”, 2025, accepted for publication in “Behaviour & Information Technology”. DOI: 10.1080/0144929X.2025.2456068.
Tipologia:
Documento in Post-print
Licenza:
Creative commons
Dimensione
4.4 MB
Formato
Adobe PDF
|
4.4 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.