A 3D Virtual Environment for Exploratory Learning in Mobile Robot Control

©2005 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Presented at the 2005 IEEE International Conference on Systems, Man and Cybernetics, Waikoloa, Hawaii, USA, October 10-12, 2005.DOI: 10.1109/ICSMC.2005.1571163This paper discusses a virtual environment that enables human agents to develop the skills necessary to control a mobile robot through the implementation of exploratory learning practices. The interface connects the human user to both a virtual and physical robot resident in the real-world, and allows evaluation of human performance using a framework that analyzes execution parameters during human operation. The execution data is then used to compare the capability of human agents to learn the skill sets necessary to control the robot during a novel task situation. We give an overview of the environment, as well as the experimental results comparing the performance of multiple operators learning to control a virtual robot

A Methodology to Assess Performance of Human-Robotic Systems in Achievement of Collective Tasks

©2005 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2005) Edmonton, Canada, August 2005.DOI: 10.1109/IROS.2005.1545010In this paper, we present a methodology to assess system performance of human-robotic systems in achievement of collective tasks such as habitat construction, geological sampling, and space exploration. The methodology uses a systematic approach that assesses performance by incorporating capabilities of both human and robotic agents based on accomplishment of functional operations and effect of cognitive stress due to continuous operation by the human agent. In this paper, we provide an overview of the assessment system and discuss its implementation on a representative habitat construction task

A Novel Approach for Performance Assessment of Human-Robotic Interaction

Robots have always been touted as powerful tools that could be used effectively in a number of applications ranging from automation to human-robot interaction. In order for such systems to operate adequately and safely in the real world, they must be able to perceive, and must have abilities of reasoning up to a certain level. Toward this end, performance evaluation metrics are used as important measures. This research work is intended to be a further step toward identifying common metrics for task-oriented human-robot interaction. We believe that within the context of human-robot interaction systems, both humans' and robots' actions and interactions (jointly and independently) can significantly affect the quality of the accomplished task. As such, our goal becomes that of providing a foundation upon which we can assess how well the human and the robot perform as a team. Thus, we propose a generic performance metric to assess the performance of the human-robot team, where one or more robots are involved. Sequential and parallel robot cooperation schemes with varying levels of task dependency are considered, and the proposed performance metric is augmented and extended to accommodate such scenarios. This is supported by some intuitively derived mathematical models and some advanced numerical simulations. To efficiently model such a metric, we propose a two-level fuzzy temporal model to evaluate and estimate the human trust in automation, while collaborating and interacting with robots and machines to complete some tasks. Trust modelling is critical, as it directly influences the interaction time that should be directly and indirectly dedicated toward interacting with the robot. Another fuzzy temporal model is also presented to evaluate the human reliability during interaction time. A significant amount of research work stipulates that system failures are due almost equally to humans as to machines, and therefore, assessing this factor in human-robot interaction systems is crucial. The proposed framework is based on the most recent research work in the areas of human-machine interaction and performance evaluation metrics. The fuzzy knowledge bases are further updated by implementing an application robotic platform where robots and users interact via semi-natural language to achieve tasks with varying levels of complexity and completion rates. User feedback is recorded and used to tune the knowledge base where needed. This work intends to serve as a foundation for further quantitative research to evaluate the performance of the human-robot teams in achievement of collective tasks