Human-Robot Interaction and Perceived Irrationality: A Study of Trust Dynamics and Error Acknowledgment

• URL: http://arxiv.org/abs/2403.14293v2
• Date: Tue, 06 May 2025 21:25:02 GMT
• Title: Human-Robot Interaction and Perceived Irrationality: A Study of Trust Dynamics and Error Acknowledgment
• Authors: Ponkoj Chandra Shill, Md. Azizul Hakim,
• Abstract summary: This study systematically examines trust dynamics and system design by analyzing human reactions to robot failures.<n>We conducted a four-stage survey to explore how trust evolves throughout human-robot interactions.<n>Results indicate that trust in robotic systems significantly increased when robots acknowledged their errors or limitations.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: As robots become increasingly integrated into various industries, understanding how humans respond to robotic failures is critical. This study systematically examines trust dynamics and system design by analyzing human reactions to robot failures. We conducted a four-stage survey to explore how trust evolves throughout human-robot interactions. The first stage collected demographic data and initial trust levels. The second stage focused on preliminary expectations and perceptions of robotic capabilities. The third stage examined interaction details, including robot precision and error acknowledgment. Finally, the fourth stage assessed post-interaction perceptions, evaluating trust dynamics, forgiveness, and willingness to recommend robotic technologies. Results indicate that trust in robotic systems significantly increased when robots acknowledged their errors or limitations. Additionally, participants showed greater willingness to suggest robots for future tasks, highlighting the importance of direct engagement in shaping trust dynamics. These findings provide valuable insights for designing more transparent, responsive, and trustworthy robotic systems. By enhancing our understanding of human-robot interaction (HRI), this study contributes to the development of robotic technologies that foster greater public acceptance and adoption.

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)
• GNN-based Decentralized Perception in Multirobot Systems for Predicting Worker Actions [12.260881600042374]
  This paper introduces a perception framework that enables mobile robots to understand and share information about human actions in a decentralized way. A swarm-inspired decision-making process is used to ensure all robots agree on a unified interpretation of the human's actions.
  arXiv  Detail & Related papers  (2025-01-08T00:06:38Z)
• A Retrospective on the Robot Air Hockey Challenge: Benchmarking Robust, Reliable, and Safe Learning Techniques for Real-world Robotics [53.33976793493801]
  We organized the Robot Air Hockey Challenge at the NeurIPS 2023 conference. We focus on practical challenges in robotics, such as the sim-to-real gap, low-level control issues, safety problems, real-time requirements, and the limited availability of real-world data. Results show that solutions combining learning-based approaches with prior knowledge outperform those relying solely on data when real-world deployment is challenging.
  arXiv  Detail & Related papers  (2024-11-08T17:20:47Z)
• On the Effect of Robot Errors on Human Teaching Dynamics [1.7249361224827533]
  We investigate how the presence and severity of robot errors affect three dimensions of human teaching dynamics. Results show that people tend to spend more time teaching robots with errors. Our findings offer valuable insights for designing effective interfaces for interactive learning.
  arXiv  Detail & Related papers  (2024-09-15T19:02:34Z)
• Common (good) practices measuring trust in HRI [55.2480439325792]
  Trust in robots is widely believed to be imperative for the adoption of robots into people's daily lives. Researchers have been exploring how people trust robot in different ways. Most roboticists agree that insufficient levels of trust lead to a risk of disengagement.
  arXiv  Detail & Related papers  (2023-11-20T20:52:10Z)
• "Do it my way!": Impact of Customizations on Trust perceptions in Human-Robot Collaboration [0.8287206589886881]
  Personalization of assistive robots is positively correlated with robot adoption and user perceptions. Our findings indicate that increased levels of customization was associated with higher trust and comfort perceptions.
  arXiv  Detail & Related papers  (2023-10-28T19:31:40Z)
• 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)
• 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)
• 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)
• Evaluation of Performance-Trust vs Moral-Trust Violation in 3D Environment [1.4502611532302039]
  We aim to design an experiment to investigate the consequences of performance-trust violation and moral-trust violation in a search and rescue scenario. We want to see if two similar robot failures, one caused by a performance-trust violation and the other by a moral-trust violation have distinct effects on human trust.
  arXiv  Detail & Related papers  (2022-06-30T17:27:09Z)
• 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)
• Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning [121.9708998627352]
  Recent work has shown that, in practical robot learning applications, the effects of adversarial training do not pose a fair trade-off. This work revisits the robustness-accuracy trade-off in robot learning by analyzing if recent advances in robust training methods and theory can make adversarial training suitable for real-world robot applications.
  arXiv  Detail & Related papers  (2022-04-15T08:12:15Z)
• Explain yourself! Effects of Explanations in Human-Robot Interaction [10.389325878657697]
  Explanations of robot decisions could affect user perceptions, justify their reliability, and increase trust. The effects on human perceptions of robots that explain their decisions have not been studied thoroughly. This study demonstrates the need for and potential of explainable human-robot interaction.
  arXiv  Detail & Related papers  (2022-04-09T15:54:27Z)
• A Review on Trust in Human-Robot Interaction [0.0]
  A new field of research in human-robot interaction, namely human-robot trust, is emerging. This paper reviews the past works on human-robot trust based on the research topics and discuss selected trends in this field.
  arXiv  Detail & Related papers  (2021-05-20T21:50:03Z)
• Show Me What You Can Do: Capability Calibration on Reachable Workspace for Human-Robot Collaboration [83.4081612443128]
  We show that a short calibration using REMP can effectively bridge the gap between what a non-expert user thinks a robot can reach and the ground-truth. We show that this calibration procedure not only results in better user perception, but also promotes more efficient human-robot collaborations.
  arXiv  Detail & Related papers  (2021-03-06T09:14:30Z)
• Affect-Driven Modelling of Robot Personality for Collaborative Human-Robot Interactions [16.40684407420441]
  Collaborative interactions require social robots to adapt to the dynamics of human affective behaviour. We propose a novel framework for personality-driven behaviour generation in social robots.
  arXiv  Detail & Related papers  (2020-10-14T16:34:14Z)
• Human Grasp Classification for Reactive Human-to-Robot Handovers [50.91803283297065]
  We propose an approach for human-to-robot handovers in which the robot meets the human halfway. We collect a human grasp dataset which covers typical ways of holding objects with various hand shapes and poses. We present a planning and execution approach that takes the object from the human hand according to the detected grasp and hand position.
  arXiv  Detail & Related papers  (2020-03-12T19:58:03Z)

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.