Improved Trust in Human-Robot Collaboration with ChatGPT
- URL: http://arxiv.org/abs/2304.12529v1
- Date: Tue, 25 Apr 2023 02:48:35 GMT
- Title: Improved Trust in Human-Robot Collaboration with ChatGPT
- Authors: Yang Ye, Hengxu You, Jing Du
- Abstract summary: 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.
- Score: 1.086544864007391
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Human robot collaboration is becoming increasingly important as robots become
more involved in various aspects of human life in the era of Artificial
Intelligence. However, the issue of human operators trust in robots remains a
significant concern, primarily due to the lack of adequate semantic
understanding and communication between humans and robots. The emergence of
Large Language Models (LLMs), such as ChatGPT, provides an opportunity to
develop an interactive, communicative, and robust human-robot collaboration
approach. This paper explores the impact of ChatGPT on trust in a human-robot
collaboration assembly task. This study designs a robot control system called
RoboGPT using ChatGPT to control a 7-degree-of-freedom robot arm to help human
operators fetch, and place tools, while human operators can communicate with
and control the robot arm using natural language. A human-subject experiment
showed that incorporating ChatGPT in robots significantly increased trust in
human-robot collaboration, which can be attributed to the robot's ability to
communicate more effectively with humans. Furthermore, ChatGPT ability to
understand the nuances of human language and respond appropriately helps to
build a more natural and intuitive human-robot interaction. The findings of
this study have significant implications for the development of human-robot
collaboration systems.
Related papers
- HARMONIC: Cognitive and Control Collaboration in Human-Robotic Teams [0.0]
We demonstrate a cognitive strategy for robots in human-robot teams that incorporates metacognition, natural language communication, and explainability.
The system is embodied using the HARMONIC architecture that flexibly integrates cognitive and control capabilities.
arXiv Detail & Related papers (2024-09-26T16:48:21Z) - Exploring Large Language Models to Facilitate Variable Autonomy for Human-Robot Teaming [4.779196219827508]
We introduce a novel framework for a GPT-powered multi-robot testbed environment, based on a Unity Virtual Reality (VR) setting.
This system allows users to interact with robot agents through natural language, each powered by individual GPT cores.
A user study with 12 participants explores the effectiveness of GPT-4 and, more importantly, user strategies when being given the opportunity to converse in natural language within a multi-robot environment.
arXiv Detail & Related papers (2023-12-12T12:26:48Z) - 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) - 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) - CoGrasp: 6-DoF Grasp Generation for Human-Robot Collaboration [0.0]
We propose a novel, deep neural network-based method called CoGrasp that generates human-aware robot grasps.
In real robot experiments, our method achieves about 88% success rate in producing stable grasps.
Our approach enables a safe, natural, and socially-aware human-robot objects' co-grasping experience.
arXiv Detail & Related papers (2022-10-06T19:23:25Z) - Robots with Different Embodiments Can Express and Influence Carefulness
in Object Manipulation [104.5440430194206]
This work investigates the perception of object manipulations performed with a communicative intent by two robots.
We designed the robots' movements to communicate carefulness or not during the transportation of objects.
arXiv Detail & Related papers (2022-08-03T13:26:52Z) - 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) - 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) - Joint Mind Modeling for Explanation Generation in Complex Human-Robot
Collaborative Tasks [83.37025218216888]
We propose a novel explainable AI (XAI) framework for achieving human-like communication in human-robot collaborations.
The robot builds a hierarchical mind model of the human user and generates explanations of its own mind as a form of communications.
Results show that the generated explanations of our approach significantly improves the collaboration performance and user perception of the robot.
arXiv Detail & Related papers (2020-07-24T23:35:03Z)
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.