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Full-Text Articles in Physical Sciences and Mathematics
From Qualitative To Quantitative: Supporting Robot Understanding In Human-Interactive Path Planning, Daqing Yi
From Qualitative To Quantitative: Supporting Robot Understanding In Human-Interactive Path Planning, Daqing Yi
Theses and Dissertations
Improvements in robot autonomy are changing human-robot interaction from low-level manipulation to high-level task-based collaboration. When a robot can independently and autonomously executes tasks, a human in a human-robot team acts as a collaborator or task supervisor instead of a tele-operator. When applying this to planning paths for a robot's motion, it is very important that the supervisor's qualitative intent is translated into aquantitative model so that the robot can produce a desirable consequence. In robotic path planning, algorithms can transform a human's qualitative requirement into a robot's quantitative model so that the robot behavior satisfies the human's intent. In …
Using A Model Of Temporal Latency To Improve Supervisory Control Of Human-Robot Teams, Kyle Lee Blatter
Using A Model Of Temporal Latency To Improve Supervisory Control Of Human-Robot Teams, Kyle Lee Blatter
Theses and Dissertations
When humans and remote robots work together on a team, the robots always interact with a human supervisor, even if the interaction is limited to occasional reports. Distracting a human with robotic interactions doesn't pose a problem so long as the inclusion of robots increases the team's overall effectiveness. Unfortunately, increasing the supervisor's cognitive load may decrease the team's sustainable performance to the point where robotic agents are more a liability than an asset. Present approaches resolve this problem with adaptive autonomy, where a robot changes its level of autonomy based on the supervisor's cognitive load. This thesis proposes to …
Managing Autonomy By Hierarchically Managing Information: Autonomy And Information At The Right Time And The Right Place, Rongbin Lin
Theses and Dissertations
When working with a complex AI or robotics system in a specific application, users often need to incorporate their special domain knowledge into the autonomous system. Such needs call for the ability to manage autonomy. However, managing autonomy can be a difficult task because the internal mechanisms and algorithms of the autonomous components may be beyond the users' understanding. We propose an approach where users manage autonomy indirectly by managing information provided to the intelligent system hierarchically at three different temporal scales: strategic, between-episodes, and within-episode. Information management tools at multiple temporal scales allow users to influence the autonomous behaviors …