Prior studies have found that providing explanations about robots' decisions and actions help to improve system transparency, increase human users' trust of robots , and enable effective human-robot collaboration. Different users have various preferences about what should be included in explanations. However, little research has been conducted for the generation of personalized explanations. In this paper, we present a system for generating personalized explanations of robotic planning via user feedback. We consider robotic planning using Markov decision processes (MDPs) and develop an algorithm to automatically generate a personalized explanation of an optimal robotic plan (i.e., an optimal MDP policy) based on the user preference regarding four elements (i.e., objective, locality, specificity, and abstraction ). In addition, we design the system to interact with users via answering users' further questions about the generated explanations. Users have the option to update their preferences to view different explanations. The system is capable of detecting and resolving any preference conflict via user interaction. Our user study results show that the generated personalized explanations improve user satisfaction, while the majority of users liked the system's capabilities of question-answering , and conflict detection and resolution.
Prior studies have found that explaining robot decisions and actions helps to increase system transparency, improve user understanding , and enable effective human-robot collaboration. In this paper, we present a system for generating personalized explanations of robot path planning via user feedback. We consider a robot navigating in an environment modeled as a Markov decision process (MDP), and develop an algorithm to automatically generate a personalized explanation of an optimal MDP policy, based on the user preference regarding four elements (i.e., objective, locality, specificity, and corpus ). In addition, we design the system to interact with users via answering users' further questions about the generated explanations. Users have the option to update their preferences to view different explanations. The system is capable of detecting and resolving any preference conflict via user interaction. The results of an online user study show that the generated personalized explanations improve user satisfaction, while the majority of users liked the system's capabilities of question-answering and conflict detection / resolution.
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providing explanations about robots'
explaining robot
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help to improve
helps to increase
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increase human users' trust of robots
improve user understanding
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Different users have various preferences about what should be included in explanations. However, little research has been conducted for the generation of personalized explanations.
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robotic
robot path
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robotic planning using Markov decision processes (MDPs)
a robot navigating in an environment modeled as a Markov decision process (MDP),
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robotic plan (i.e., an optimal MDP policy)
MDP policy,
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abstraction
corpus
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Our user study results
The results of an online user study
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,
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and
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