Human-centered AI with focus on Human-robot interaction (Book chapter)
- URL: http://arxiv.org/abs/2507.04095v1
- Date: Sat, 05 Jul 2025 16:45:03 GMT
- Title: Human-centered AI with focus on Human-robot interaction (Book chapter)
- Authors: Alireza Mortezapour, Giuliana Vitiello,
- Abstract summary: Social robots can be considered the descendants of steam engines from the First Industrial Revolution (IR 1.0) and industrial robotic arms from the Third Industrial Revolution (IR 3.0)<n>This chapter aims to introduce humans and their needs in interactions with robots, ranging from short-term, one-on-one interactions (micro-level) to long-term, macro-level needs at the societal scale.<n>Building upon the principles of human-centered AI, this chapter presents a new framework of human needs called the Dual Pyramid.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Modern social robots can be considered the descendants of steam engines from the First Industrial Revolution (IR 1.0) and industrial robotic arms from the Third Industrial Revolution (IR 3.0). As some time has passed since the introduction of these robots during the Fourth Industrial Revolution (IR 4.0), challenges and issues in their interaction with humans have emerged, leading researchers to conclude that, like any other AI-based technology, these robots must also be human-centered to meet the needs of their users. This chapter aims to introduce humans and their needs in interactions with robots, ranging from short-term, one-on-one interactions (micro-level) to long-term, macro-level needs at the societal scale. Building upon the principles of human-centered AI, this chapter presents, for the first time, a new framework of human needs called the Dual Pyramid. This framework encompasses a comprehensive list of human needs in robot interactions, from the most fundamental, robot effectiveness to macro level requirements, such as the collaboration with robots in achieving the United Nations 17 Sustainable Development Goals.
Related papers
- A roadmap for AI in robotics [55.87087746398059]
We are witnessing growing excitement in robotics at the prospect of leveraging the potential of AI to tackle some of the outstanding barriers to the full deployment of robots in our daily lives.<n>This article offers an assessment of what AI for robotics has achieved since the 1990s and proposes a short- and medium-term research roadmap listing challenges and promises.
arXiv Detail & Related papers (2025-07-26T15:18:28Z) - HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation [50.616995671367704]
We present a high-dimensional, simulated robot learning benchmark, HumanoidBench, featuring a humanoid robot equipped with dexterous hands.
Our findings reveal that state-of-the-art reinforcement learning algorithms struggle with most tasks, whereas a hierarchical learning approach achieves superior performance when supported by robust low-level policies.
arXiv Detail & Related papers (2024-03-15T17:45:44Z) - Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots [119.55240471433302]
Habitat 3.0 is a simulation platform for studying collaborative human-robot tasks in home environments.
It addresses challenges in modeling complex deformable bodies and diversity in appearance and motion.
Human-in-the-loop infrastructure enables real human interaction with simulated robots via mouse/keyboard or a VR interface.
arXiv Detail & Related papers (2023-10-19T17:29:17Z) - Asch Meets HRI: Human Conformity to Robot Groups [0.9350546589421261]
We present a research outline that aims at investigating group dynamics and peer pressure in the context of industrial robots.
We are interested in highlighting the effects of group size, perceived robot credibility, psychological stress, and peer pressure in the context of industrial robots.
arXiv Detail & Related papers (2023-08-25T11:14:24Z) - Exploring AI-enhanced Shared Control for an Assistive Robotic Arm [4.999814847776098]
In particular, we explore how Artifical Intelligence (AI) can be integrated into a shared control paradigm.
In particular, we focus on the consequential requirements for the interface between human and robot.
arXiv Detail & Related papers (2023-06-23T14:19:56Z) - Improved Trust in Human-Robot Collaboration with ChatGPT [1.086544864007391]
The paper explores the impact of ChatGPT on trust in a human-robot collaboration assembly task.
A human-subject experiment showed that incorporating ChatGPT in robots significantly increased trust in human-robot collaboration.
The findings of this study have significant implications for the development of human-robot collaboration systems.
arXiv Detail & Related papers (2023-04-25T02:48:35Z) - The dynamic nature of trust: Trust in Human-Robot Interaction revisited [0.38233569758620045]
Socially assistive robots (SARs) assist humans in the real world.
Risk introduces an element of trust, so understanding human trust in the robot is imperative.
arXiv Detail & Related papers (2023-03-08T19:20:11Z) - HERD: Continuous Human-to-Robot Evolution for Learning from Human
Demonstration [57.045140028275036]
We show that manipulation skills can be transferred from a human to a robot through the use of micro-evolutionary reinforcement learning.
We propose an algorithm for multi-dimensional evolution path searching that allows joint optimization of both the robot evolution path and the policy.
arXiv Detail & Related papers (2022-12-08T15:56:13Z) - The Road to a Successful HRI: AI, Trust and ethicS-TRAITS [64.77385130665128]
The aim of this workshop is to foster the exchange of insights on past and ongoing research towards effective and long-lasting collaborations between humans and robots.
We particularly focus on AI techniques required to implement autonomous and proactive interactions.
arXiv Detail & Related papers (2022-06-07T11:12:45Z) - Spatial Computing and Intuitive Interaction: Bringing Mixed Reality and
Robotics Together [68.44697646919515]
This paper presents several human-robot systems that utilize spatial computing to enable novel robot use cases.
The combination of spatial computing and egocentric sensing on mixed reality devices enables them to capture and understand human actions and translate these to actions with spatial meaning.
arXiv Detail & Related papers (2022-02-03T10:04:26Z)
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.