Imperfect Automation in Scheduling Operator Attention on Control of Multi-Robots

Chien, Shih‐Yi; Lewis, Michael; Mehrotra, Siddharth; Sycara, Katia

doi:10.1177/1541931213571260

Cited by 12 publications

(10 citation statements)

References 21 publications

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“…Proof. The result follows from the observation that using (8) and Def. 1, we can re-write the passive set as…”

Section: Indexability Of the Assistance Problemmentioning

confidence: 70%

Scalable Operator Allocation for Multi-Robot Assistance: A Restless Bandit Approach

Dahiya¹,

Akbarzadeh²,

Mahajan³

et al. 2021

Preprint

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In this paper, we consider the problem of allocating human operators in a system with multiple semiautonomous robots. Each robot is required to perform an independent sequence of tasks, subjected to a chance of failing and getting stuck in a fault state at every task. If and when required, a human operator can assist or teleoperate a robot. Conventional MDP techniques used to solve such problems face scalability issues due to exponential growth of state and action spaces with the number of robots and operators. In this paper we derive conditions under which the operator allocation problem is indexable, enabling the use of the Whittle index heuristic. The conditions can be easily checked to verify indexability, and we show that they hold for a wide range of problems of interest. Our key insight is to leverage the structure of the value function of individual robots, resulting in conditions that can be verified separately for each state of each robot. We apply these conditions to two types of transitions commonly seen in remote robot supervision systems. Through numerical simulations, we demonstrate the efficacy of Whittle index policy as a near-optimal and scalable approach that outperforms existing scalable methods.

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“…Proof. The result follows from the observation that using (8) and Def. 1, we can re-write the passive set as…”

Section: Indexability Of the Assistance Problemmentioning

confidence: 70%

Scalable Operator Allocation for Multi-Robot Assistance: A Restless Bandit Approach

Dahiya¹,

Akbarzadeh²,

Mahajan³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…As a result, the discounted reward collected after the time at which the loop is formed is negligible according to equation (3). Therefore, Q π * s s can still approximate the optimal value function of the dynamic graph traversal problem (4) well with the bound given by equation (7).…”

Section: A Static Gtp Approximationmentioning

confidence: 83%

“…An interesting fact that we observe on the bound developed in Theorem 1 is that the approximation error is separated into two parts and the discount factor λ creates a tension between the two terms on the right hand side of the inequality (7). The first term encapsulates the error generated by the time-varying nature of the rewards and costs in the dynamic problem and can be damped by a large λ, by applying which the further nodes on the path could make less contribution to the value function compared to the nearer nodes.…”

Section: A Static Gtp Approximationmentioning

confidence: 90%

“…However, optimally identifying which robot to assist in which order in an uncertain environment is a challenging task for human supervisors. As shown in recent user studies on operating multi-robot teams, a single operator can get overwhelmed by the number of requests and messages, resulting in sub-optimal performance [6,7,23]. Moreover, the increase of the number of robots can cause further performance degradation of the human-robot team [31].…”

Section: Introductionmentioning

confidence: 99%

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Traversing Supervisor Problem: An Approximately Optimal Approach to Multi-Robot Assistance

Ji¹,

Dong²,

Driggs-Campbell³

2022

Preprint

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The number of multi-robot systems deployed in field applications has increased dramatically over the years. Despite the recent advancement of navigation algorithms, autonomous robots often encounter challenging situations where the control policy fails and the human assistance is required to resume robot tasks. Human-robot collaboration can help achieve high-levels of autonomy, but monitoring and managing multiple robots at once by a single human supervisor remains a challenging problem. Our goal is to help a supervisor decide which robots to assist in which order such that the team performance can be maximized. We formulate the one-to-many supervision problem in uncertain environments as a dynamic graph traversal problem. An approximation algorithm based on the profitable tour problem on a static graph is developed to solve the original problem, and the approximation error is bounded and analyzed. Our case study on a simulated autonomous farm demonstrates superior team performance than baseline methods in task completion time and human working time, and that our method can be deployed in real-time for robot fleets with moderate size.

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“…The HMM analysis was therefore included in our analysis to further access the operator's cognitive strategies via the emission and transition probabilities. A small portion of the preliminary results of the conventional performance analysis (number of victim detections and failures resolved, and workload scores) was presented in (Chien et al, 2013(Chien et al, , 2012c; however, this paper adopted more precise measures (e.g., victim finding and missing rates) to determine the differences and applied the HMM approach to scrutinize the cognitive variances.…”

Section: Transition Probabilitymentioning

confidence: 99%

Attention allocation for human multi-robot control: Cognitive analysis based on behavior data and hidden states

Chien

Lin

Lee

et al. 2018

International Journal of Human-Computer Studies

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Human multi-robot interaction exploits both the human operator's high-level decision-making skills and the robotic agents' vigorous computing and motion abilities. While controlling multi-robot teams, an operator's attention must constantly shift between individual robots to maintain sufficient situation awareness. To conserve an operator's attentional resources, a robot with self-reflect capability on its abnormal status can help an operator focus her attention on emergent tasks rather than unneeded routine checks. With the proposing self-reflect aids, the human-robot interaction becomes a queuing framework, where the robots act as the clients to request for interaction and an operator acts as the server to respond these job requests. This paper examined two types of queuing schemes, the self-paced Open-queue identifying all robots' normal/abnormal conditions, whereas the forced-paced shortest-job-first (SJF) queue showing a single robot's request at one time by following the SJF approach. As a robot may miscarry its experienced failures in various situations, the effects of imperfect automation were also investigated in this paper. The results suggest that the SJF attentional scheduling approach can provide stable performance in both primary (locate potential targets) and secondary (resolve robots' failures) tasks, regardless of the system's reliability levels. However, the conventional results (e.g., number of targets marked) only present little information about users' underlying cognitive strategies and may fail to reflect the user's true intent. As understanding users' intentions is critical to providing appropriate cognitive aids to enhance task performance, a Hidden Markov Model (HMM) is used to examine operators' underlying cognitive intent and identify the unobservable cognitive states. The HMM results demonstrate fundamental differences among the queuing mechanisms and reliability conditions. The findings suggest that HMM can be helpful in investigating the use of human cognitive resources under multitasking environments.

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Imperfect Automation in Scheduling Operator Attention on Control of Multi-Robots

Cited by 12 publications

References 21 publications

Scalable Operator Allocation for Multi-Robot Assistance: A Restless Bandit Approach

Scalable Operator Allocation for Multi-Robot Assistance: A Restless Bandit Approach

Traversing Supervisor Problem: An Approximately Optimal Approach to Multi-Robot Assistance

Attention allocation for human multi-robot control: Cognitive analysis based on behavior data and hidden states

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