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Robot-mediated interventions are one promising and novel approach for encouraging motor exploration in young children, but knowledge about the effectiveness of toy-like features for child-robot interaction is limited. We were interested in understanding the characteristics of current toys to inform the design of interactive abilities for assistive robots. This work first provides a systematic review of toy characteristics in n=154 Fisher-Price products and then analyzes the effectiveness of common and uncommon toy-like behaviors from our custom assistive robot. Toy review results showed that light and sound features were significantly more common than bubbles, wheels, and self-propulsion. Exploratory play sessions with our assistive robot showed that bubbles were significantly more successful at encouraging child motion than other robot behaviors. Further, all studied robot behaviors demonstrated the capability to encourage child motion. The products of this work can inform the efforts of human-robot interaction and child development experts who study child mobility interventions.
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This content will become publicly available on June 30, 2026
GoBot: An Autonomous Assistive Robot Using Behavior Trees to Encourage Child Mobility
In early motor interventions from clinical rehabilitation to physical activity encouragement, one major challenge is maintaining child engagement and motivation. Robots show unique promise for addressing this challenge, but providing robots with new types of autonomous functionality is vital for promoting robot integration and usefulness in the clinic and home spaces. To provide needed autonomy capabilities for GoBot, our assistive robot for child–robot motion interventions, we propose a behavior tree framework. Within our framework, we build two trees: one manually designed based on expert knowledge of the child–robot interaction domain, and a second automatically synthesized and requiring minimal human input and time to construct. We tested each behavior tree withN= 11 children who interacted with GoBot during two behavior tree phases and a stationary-robot control phase. Our results show that both behavior tree phases tended to yield more child motion and significantly higher parent perception of child engagement, compared to the control phase. We showed that GoBot, equipped with our framework, has the potential to encourage movement and interaction in children and that a synthesized tree can be competitive with a manually designed tree. The products of this work can benefit researchers of behavior trees and child–robot interaction.
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- Award ID(s):
- 2024950
- PAR ID:
- 10590450
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM Transactions on Human-Robot Interaction
- Volume:
- 14
- Issue:
- 3
- ISSN:
- 2573-9522
- Page Range / eLocation ID:
- 1 to 17
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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