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 effects of human inputs on the swarm's behavior. Both of them are very challenging. Prior work in the literature has shown that inserting the human input as soon as possible to divert the swarm from its original goal towards the new goal does not always result in optimal performance (measured by some criterion such as the total time required by the swarm to reach the second goal). This phenomenon has been called Neglect Benevolence, conveying the idea that in many cases it is preferable to neglect the swarm for some time before inserting human input. In this paper, we study how humans can develop an understanding of swarm dynamics so they can predict the effects of the timing of their input on the state and performance of the swarm. We developed the swarm configuration shape-changing Neglect Benevolence Task as a Human Swarm Interaction (HSI) reference task allowing comparison between human and optimal input timing performance in control of swarms. Our results show that humans can learn to approximate optimal timing and that displays which make consensus variables perceptually accessible can enhance performance.

Human-Robot Swarm Interaction with Limited Situational Awareness

“…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.…”

“…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.…”

Characterizing human perception of emergent swarm behaviors

“…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).…”

“…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].…”

The effect of display type on operator prediction of future swarm states

“…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.…”