From Modelling to Understanding Children's Behaviour in the Context of
Robotics and Social Artificial Intelligence
- URL: http://arxiv.org/abs/2210.11161v1
- Date: Thu, 20 Oct 2022 10:58:42 GMT
- Title: From Modelling to Understanding Children's Behaviour in the Context of
Robotics and Social Artificial Intelligence
- Authors: Serge Thill and Vicky Charisi and Tony Belpaeme and Ana Paiva
- Abstract summary: This workshop aims to promote a common ground among different disciplines such as developmental sciences, artificial intelligence and social robotics.
We will discuss cutting-edge research in the area of user modelling and adaptive systems for children.
- Score: 3.6017760602154576
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Understanding and modelling children's cognitive processes and their
behaviour in the context of their interaction with robots and social artificial
intelligence systems is a fundamental prerequisite for meaningful and effective
robot interventions. However, children's development involve complex faculties
such as exploration, creativity and curiosity which are challenging to model.
Also, often children express themselves in a playful way which is different
from a typical adult behaviour. Different children also have different needs,
and it remains a challenge in the current state of the art that those of
neurodiverse children are under-addressed. With this workshop, we aim to
promote a common ground among different disciplines such as developmental
sciences, artificial intelligence and social robotics and discuss cutting-edge
research in the area of user modelling and adaptive systems for children.
Related papers
- Exploring Parent's Needs for Children-Centered AI to Support Preschoolers' Interactive Storytelling and Reading Activities [52.828843153565984]
AI-based storytelling and reading technologies are becoming increasingly ubiquitous in preschoolers' lives.
This paper investigates how they function in practical storytelling and reading scenarios and, how parents, the most critical stakeholders, experience and perceive them.
Our findings suggest that even though AI-based storytelling and reading technologies provide more immersive and engaging interaction, they still cannot meet parents' expectations due to a series of interactive and algorithmic challenges.
arXiv Detail & Related papers (2024-01-24T20:55:40Z) - Developmental Curiosity and Social Interaction in Virtual Agents [2.8894038270224858]
We create a virtual infant agent and place it in a developmentally-inspired 3D environment with no external rewards.
We test intrinsic reward functions that are similar to motivations that have been proposed to drive exploration in humans.
We find that learning a world model in the presence of an attentive caregiver helps the infant agent learn how to predict scenarios.
arXiv Detail & Related papers (2023-05-22T18:17:07Z) - World Models and Predictive Coding for Cognitive and Developmental
Robotics: Frontiers and Challenges [51.92834011423463]
We focus on the two concepts of world models and predictive coding.
In neuroscience, predictive coding proposes that the brain continuously predicts its inputs and adapts to model its own dynamics and control behavior in its environment.
arXiv Detail & Related papers (2023-01-14T06:38:14Z) - A Socially Assistive Robot using Automated Planning in a Paediatric
Clinical Setting [4.238191207743034]
We present an ongoing project that aims to develop a social robot to help children cope with painful and distressing medical procedures.
Our approach uses automated planning as a core component for action selection.
A key capability of our system is that the robot's behaviour adapts based on the affective state of the child patient.
arXiv Detail & Related papers (2022-10-18T11:05:37Z) - Social Assistive Robotics for Autistic Children [56.524774292536264]
The goal of the project is testing autistic children's interactions with the social robot NAO.
The innovative aspect of the project is that the children robot interaction will consider the children's emotions and specific features.
arXiv Detail & Related papers (2022-09-25T18:28:19Z) - Data-driven emotional body language generation for social robotics [58.88028813371423]
In social robotics, endowing humanoid robots with the ability to generate bodily expressions of affect can improve human-robot interaction and collaboration.
We implement a deep learning data-driven framework that learns from a few hand-designed robotic bodily expressions.
The evaluation study found that the anthropomorphism and animacy of the generated expressions are not perceived differently from the hand-designed ones.
arXiv Detail & Related papers (2022-05-02T09:21:39Z) - Cognitive architecture aided by working-memory for self-supervised
multi-modal humans recognition [54.749127627191655]
The ability to recognize human partners is an important social skill to build personalized and long-term human-robot interactions.
Deep learning networks have achieved state-of-the-art results and demonstrated to be suitable tools to address such a task.
One solution is to make robots learn from their first-hand sensory data with self-supervision.
arXiv Detail & Related papers (2021-03-16T13:50:24Z) - Intelligent behavior depends on the ecological niche: Scaling up AI to
human-like intelligence in socio-cultural environments [17.238068736229017]
This paper outlines a perspective on the future of AI, discussing directions for machines models of human-like intelligence.
We emphasize the role of ecological niches in sculpting intelligent behavior, and in particular that human intelligence was fundamentally shaped to adapt to a constantly changing socio-cultural environment.
arXiv Detail & Related papers (2021-03-11T16:24:00Z) - Sensorimotor representation learning for an "active self" in robots: A
model survey [10.649413494649293]
In humans, these capabilities are thought to be related to our ability to perceive our body in space.
This paper reviews the developmental processes of underlying mechanisms of these abilities.
We propose a theoretical computational framework, which aims to allow the emergence of the sense of self in artificial agents.
arXiv Detail & Related papers (2020-11-25T16:31:01Z) - A Developmental Neuro-Robotics Approach for Boosting the Recognition of
Handwritten Digits [91.3755431537592]
Recent evidence shows that a simulation of the children's embodied strategies can improve the machine intelligence too.
This article explores the application of embodied strategies to convolutional neural network models in the context of developmental neuro-robotics.
arXiv Detail & Related papers (2020-03-23T14:55:00Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.