Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction 2015
DOI: 10.1145/2696454.2696470
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Bounds of Neglect Benevolence in Input Timing for Human Interaction with Robotic Swarms

Abstract: Robotic swarms are distributed systems whose members interact via local control laws to achieve a variety of behaviors, such as flocking. In many practical applications, human operators may need to change the current behavior of a swarm from the goal that the swarm was going towards into a new goal due to dynamic changes in mission objectives. There are two related but distinct capabilities needed to supervise a robotic swarm. The first is comprehension of the swarm's state and the second is prediction of the … Show more

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Cited by 36 publications
(40 citation statements)
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“…In these systems the performance deteriorates due to periods of neglect. In our study we observed positive effects and some learning of neglect benevolence dynamics by experienced operators, further supporting the evidence from [13] that human operators can learn to adapt the timing of their commands to the neglect benevolence of the swarm.…”
Section: Related Worksupporting
confidence: 88%
See 1 more Smart Citation
“…In these systems the performance deteriorates due to periods of neglect. In our study we observed positive effects and some learning of neglect benevolence dynamics by experienced operators, further supporting the evidence from [13] that human operators can learn to adapt the timing of their commands to the neglect benevolence of the swarm.…”
Section: Related Worksupporting
confidence: 88%
“…Neglect benevolence [19,13] is a concept that is concerned with the dynamic nature of emergent behaviors. Most swarm algorithms require time to converge to an emergent behavior and should their dynamics be disturbed, for example, by interacting with an operator, convergence may be delayed and the interaction may be detrimental.…”
Section: Related Workmentioning
confidence: 99%
“…In [16], the authors investigated whether operators presented with an HSI reference task that benefited from neglect benevolence could learn to approximate optimal input timing, after gaining experience interacting with the swarm (implicit learning). This study divided subjects into two groups that were each tasked with diverting a swarm headed from an initial state to a first configuration, and then to a different, second configuration (see Figure 1).…”
Section: A Neglect Benevolencementioning
confidence: 99%
“…For each trial in the motivating study, participants were asked to apply the input at whatever time they thought would minimize the total time required for the robots to converge to Formation 2, after initially moving to Formation 1. This figure is taken from [16].…”
Section: A Neglect Benevolencementioning
confidence: 99%
“…In [13], the authors show through simulations that this phenomenon does indeed exist, and propose an algorithm for computing optimal input times for certain cases. A follow up study in [14] demonstrated that human operators could also learn to recognize and adapt to neglect benevolence.…”
Section: Related Workmentioning
confidence: 99%