Improving Trust-Guided Behavior Adaptation Using Operator Feedback

Floyd, Michael W.; Drinkwater, Michael J.; Aha, David W.

doi:10.1007/978-3-319-24586-7_10

Cited by 8 publications

(4 citation statements)

References 17 publications

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“…This is the opposite to a number of other conceptions of HRI where the switch from autonomous to manual mode results from a loss of trust in the robot (e.g. the inverse trust metric in [22] that a robot uses to adapt behavior to increase operator's trust). Also note that here we focus on the high-level switching strategies; the low-level continuous robot motion execution under either manual or autonomous motion planning is still automatic.…”

mentioning

confidence: 71%

Trust-Based Multi-Robot Symbolic Motion Planning with a Human-in-the-Loop

Wang

Humphrey

Liao

et al. 2018

ACM Trans. Interact. Intell. Syst.

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Symbolic motion planning for robots is the process of specifying and planning robot tasks in a discrete space, then carrying them out in a continuous space in a manner that preserves the discrete-level task specifications. Despite progress in symbolic motion planning, many challenges remain, including addressing scalability for multi-robot systems and improving solutions by incorporating human intelligence. In this paper, distributed symbolic motion planning for multi-robot systems is developed to address scalability. More specifically, compositional reasoning approaches are developed to decompose the global planning problem, and atomic propositions for observation, communication, and control are proposed to address inter-robot collision avoidance.To improve solution quality and adaptability, a dynamic, quantitative, and probabilistic human-to-robot trust model is developed to aid this decomposition. Furthermore, a trust-based real-time switching framework is proposed to switch between autonomous and manual motion planning for tradeoffs between task safety and efficiency. Deadlock-and livelock-free algorithms are designed to guarantee reachability of goals with a human-in-the-loop. A set of non-trivial multi-robot simulations with direct human input and trust evaluation are provided demonstrating the successful implementation of the trust-based multi-robot symbolic motion planning methods.

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mentioning

confidence: 71%

Trust-Based Multi-Robot Symbolic Motion Planning with a Human-in-the-Loop

Wang

Humphrey

Liao

et al. 2018

ACM Trans. Interact. Intell. Syst.

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“…However, it may be impossible to elicit a complete set of rules for trustworthy behaviors if the robot needs to handle changes in teammates, environments, or mission contexts. Therefore, this work developed a trust model and a case-based reasoning framework to enable a robot to determine when to adapt its behavior to be more trustworthy for the team [126]. Behaviors that the robot itself can directly control were defined as the modifiable component, e.g., speed, obstacle padding, scan time, scan distance, etc.…”

Section: A Performance-centric Algebraic Trust Modelsmentioning

confidence: 99%

“…Overall, performance-centric algebraic trust models offer an efficient trust evaluation mechanism based on observational evidence. They are created based on selective human-robot trust impacting factors, which includes robot performance as the major component [45], [109], [126]. Due to this performance-centric perspective, these works usually mix trust with trustworthiness, which is inaccurate per Remark 1 in Section II.A.…”

Section: A Performance-centric Algebraic Trust Modelsmentioning

confidence: 99%

Human Trust in Robots: A Survey on Trust Models and Their Controls/Robotics Applications

Wang,

Li,

Zheng

et al. 2024

IEEE Open J. Control. Syst.

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Trust model is a topic that first gained interest in organizational studies and then human factors in automation. Thanks to recent advances in human-robot interaction (HRI) and human-autonomy teaming, human trust in robots has gained growing interest among researchers and practitioners. This article focuses on a survey of computational models of human-robot trust and their applications in robotics and robot controls. The motivation is to provide an overview of the state-of-the-art computational methods to quantify trust so as to provide feedback and situational awareness in HRI. Different from other existing survey papers on human-robot trust models, we seek to provide in-depth coverage of the trust model categorization, formulation, and analysis, with a focus on their utilization in robotics and robot controls. The paper starts with a discussion of the difference between human-robot trust with general agent-agent trust, interpersonal trust, and human trust in automation and machines. A list of impacting factors for human-robot trust and different trust measurement approaches, and their corresponding scales are summarized. We then review existing computational human-robot trust models and discuss the pros and cons of each category of models. These include performance-centric algebraic, time-series, Markov decision process (MDP)/Partially Observable MDP (POMDP)-based, Gaussian-based, and dynamic Bayesian network (DBN)-based trust models. Following the summary of each computational human-robot trust model, we examine its utilization in robot control applications, if any. We also enumerate the main limitations and open questions in this field and discuss potential future research directions.

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“…In situations where the agent believes its behavior is untrustworthy, it can modify its behavior in an attempt to learn (and apply) a more trustworthy behavior, thus implementing a form of adaptive autonomy. Preliminary studies in limited simulations have shown that an agent using an inverse trust method can successfully adapt its behavior given implicit feedback [15], and can benefit further from explicit feedback [16] as well as the ability to generate explanations when it modifies its behaviors [14].…”

Section: Adaptive Autonomy and Inverse Trustmentioning

confidence: 99%

Goal Reasoning and Trusted Autonomy

Johnson

Floyd

Coman

et al. 2018

Foundations of Trusted Autonomy

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An important consideration for any autonomous system is the need to react intelligently to unplanned events and observations. Doing so requires that the system be given the freedom and ability to adjust its behavior, without being commanded to do so by a human operator or external system. A good deal of research in robotics and autonomy focuses on developing systems that can change their actions and plans, to more reliably or more optimally achieve their goals. However, it is also important to develop systems that autonomously deliberate on the goals themselves. Goal Reasoning (e.g., [38]) is the study of how autonomous agents can dynamically reason about and adjust their goals. Doing so enables agents to adapt intelligently to changing conditions and unexpected events, allowing them to address a wider variety of complex problems. Section 1.3 argues for the generality of reward maximization for goals; Goal Reasoning, then, would allow the autonomous agent to adapt its own reward function. Goal Reasoning capabilities, in one form or another, may prove useful in many applications. In domains where the agents must operate autonomously for substantial periods of time or with limited communications, such as in unmanned underwater B. Johnson (B) • A.

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Improving Trust-Guided Behavior Adaptation Using Operator Feedback

Cited by 8 publications

References 17 publications

Trust-Based Multi-Robot Symbolic Motion Planning with a Human-in-the-Loop

Trust-Based Multi-Robot Symbolic Motion Planning with a Human-in-the-Loop

Human Trust in Robots: A Survey on Trust Models and Their Controls/Robotics Applications

Goal Reasoning and Trusted Autonomy

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