Personalizing Activity Selection in Assistive Social Robots from Explicit and Implicit User Feedback
Bibliographic Data
| ID | 21199397 |
|---|---|
| Authors | Marcos Maroto‐Gómez (0000-0001-9576-1731, Universidad Carlos III de Madrid, corresponding author), María Malfáz (0000-0003-2317-3329, Universidad Carlos III de Madrid), José Carlos Castillo (0000-0003-0454-9466, Universidad Carlos III de Madrid), Álvaro Castro‐González (0000-0002-5189-0002, Universidad Carlos III de Madrid), Miguel Á Salichs (0000-0002-0263-6606, Universidad Carlos III de Madrid) |
| Year | 2025 |
| Volume | 17 |
| Issue | 10 |
| Pages | 1999-2017 |
| Publication date | 2025-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Social Robotics (JOURNAL) |
| Journal identifiers | ISSN: 1875-4791 • E-ISSN: 1875-4805 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s12369-024-01124-2 |
| OpenAlex | W4394616140 |
| Language | EN |
| References cited | 38 |
Robots in multi-user environments require adaptation to produce personalized interactions. In these scenarios, the user’s feedback leads the robots to learn from experiences and use this knowledge to generate adapted activities to the user’s preferences. However, preferences are user-specific and may suffer variations, so learning is required to personalize the robot’s actions to each user. Robots can obtain feedback in Human–Robot Interaction by asking users their opinion about the activity (explicit feedback) or estimating it from the interaction (implicit feedback). This paper presents a Reinforcement Learning framework for social robots to personalize activity selection using the preferences and feedback obtained from the users. This paper also studies the role of user feedback in learning, and it asks whether combining explicit and implicit user feedback produces better robot adaptive behavior than considering them separately. We evaluated the system with 24 participants in a long-term experiment where they were divided into three conditions: (i) adapting the activity selection using the explicit feedback that was obtained from asking the user how much they liked the activities; (ii) using the implicit feedback obtained from interaction metrics of each activity generated from the user’s actions; and (iii) combining explicit and implicit feedback. As we hypothesized, the results show that combining both feedback produces better adaptive values when correlating initial and final activity scores, overcoming the use of individual explicit and implicit feedback. We also found that the kind of user feedback does not affect the user’s engagement or the number of activities carried out during the experiment
Human–computer interaction · Machine learning · Mechatronics · Robot · Robotics · Computer Science · Context-Aware Activity Recognition Systems · Reinforcement Learning in Robotics · Social Robot Interaction and HRI · Artificial Intelligence
Reinforcement Learning
Correlation Coefficients
Toward Personalized Affect-Aware Socially Assistive Robot Tutors for Long-Term Interventions with Children with Autism
Identifying Functions and Behaviours of Social Robots for In-Class Learning Activities
The Influence of Feedback Type in Robot-Assisted Training
Exploiting ability for human adaptation to facilitate improved human-robot interaction and acceptance
What if Social Robots Look for Productive Engagement
Mini
Relationship Development with Humanoid Social Robots
Learning and Personalizing Socially Assistive Robot Behaviors to Aid with Activities of Daily Living
| Citation velocity | historical |
|---|---|
| Highly cited | No |