In this paper we evaluate the scalability of human-swarm interaction (HSI) in terms of operator workload and asses the impact of three control methods on swarm performance and operator workload. Specifically, we investigate the ability of HSI to (1) overcome fanout limitations of traditional supervisory control and (2) manage a wide range of team sizes without altering the operator’s control strategy. In order to evaluate our hypothesis, we conduct a user study which we back with validation on physical robots. We evaluate three high-level control methods – leader, predator, and stakeholders – on swarms of 20, 50, and 100 individuals. We find that larger swarm sizes allow increased swarm performance without increasing operator workload, overcoming the fan-out limitations of traditional supervisory control. We further find that control style substantially affects both swarm performance and operator workload, illustrating the impact design decisions can have on the human-swarm system.

Papers from a flagship conference reflect the latest developments in the field, including work in such rapidly advancing areas as human-robot interaction and formal methods. Robotics: Science and Systems VIII spans a wide spectrum of robotics, bringing together contributions from researchers working on the mathematical foundations of robotics, robotics applications, and analysis of robotics systems. This volume presents the proceedings of the eighth annual Robotics: Science and Systems (RSS) conference, held in July 2012 at the University of Sydney. The contributions reflect the exciting diversity of the field, presenting the best, the newest, and the most challenging work on such topics as mechanisms, kinematics, dynamics and control, human-robot interaction and human-centered systems, distributed systems, mobile systems and mobility, manipulation, field robotics, medical robotics, biological robotics, robot perception, and estimation and learning in robotic systems. The conference and its proceedings reflect not only the tremendous growth of robotics as a discipline but also the desire in the robotics community for a flagship event at which the best of the research in the field can be presented.

This paper uses simulations to identify what types of human influence are afforded by the flocking and swarming structures that emerge from Couzin's bio-inspired model [4]. The goal is to allow a human to influence a decentralized agent collective without resorting to centralized human control. Evidence is provided that, when nominal agents use switching-based control to respond to human-guided predators and leaders, the resulting behavior is responsive to human input but is obtained at the cost of causing the dynamic structure of the collective to follow a single flocking structure. Leaders are more effective in influencing coherent flocks, but predators can be used to divide the flock into sub-flocks, yielding higher performance on some problems. Introducing a so-called “stakeholder” leadership style makes it possible for a human to guide the agents while maintaining several different types of structures; doing so requires more than one human-controlled agent. We then demonstrate that it is possible to produce potentially useful emergent dynamics without centralized human control, and identify an important type of emergent dynamics: automatic switches between structure types.

In this paper, we formalize the problem of human interaction with bio-inspired robot teams (HuBIRT). The formalism applies to a large class of bio-inspired team dynamics and uses simple algebraic graph theory representations to distinguish between interagent influence, environmental influence, and operator influence. These representations lead to metrics for interagent cohesiveness and responsiveness to human input. We then select two different classes of team dynamics, physicomimetics which encodes dynamics using artificial physics, and a biomimetic structure which encodes dynamics using a model of fish behavior. We then demonstrate the relevance of the metrics by conducting a series experiments that demonstrate differences between leader and predator styles of human influence, and conclude with a comparison of nearest-neighbor topologies to metric-based topologies.

This report formalizes the problem of human-interaction with bio-inspired robot teams (HuBIRT) and then systematically explores properties of various biological collectives that allow human interaction. The goal is to identify models, topologies, and control strategies that allow a decentralized agent collective to be appropriately influenced by a human. Evidence is provided that nearest neighbor topologies are more likely to be more cohesive than metric-based topologies but are less expressive. Importantly, randomly dynamic topologies appear to be as expressive as metric-based topologies and open up the possibility of probabilistically connected topologies, but this is not explored further in this report. Evidence is also provided that when nominal agents use switching-based control to respond to predators and leaders, the resulting behavior can be effectively influenced by a human and that this influence is more effective when using leaders than predators; however, the influence is obtained at the cost of changing the dynamic structure of the collective. Using non-switching controllers allows a human to influence the collective while retaining the collective’s structure, but this requires more than one leader and risks fragmenting the collective. Finally, evidence is presented that groups of stakeholders, which seek both to influence and to be influenced by other agents, can sustain influence over certain types of collectives more effectively than cohorts of leaders.