Mediating Human-Swarm Interactions Through a Robot Helper: A Shepherding-Inspired Approach

This paper presents Auto-HSI, a method for generating personalized human-swarm interaction (HSI) interfaces on demand. The objective is to enable untrained operators to use natural language descriptions and gesture demonstrations to explain how they want the robots to collectively behave in response to their gestures. Based on these inputs, the code should automatically be generated for personalized state machines that will control the robots as desired, in response to the desired gesture inputs. In the developed Auto-HSI prototype, the generated code produces a personalized interface for centralized control using one- and two-handed gestures, enabling a user to teleoperate the robots' motion, formation shape, and shape deformation. We test the gesture tracking and code generation components of Auto-HSI against performance benchmarks. We then test the full Auto-HSI prototype in ``live'' operation experiments, in which real human operators centrally control 50 simulated robots in a physics-based simulator, under nominal and noisy conditions. In these experiments, robots are teleoperated to: score a goal, traverse a maze that requires shape deformation, and score two simultaneous goals by splitting into two groups. We also demonstrate a real human operator making live updates to their personalized Auto-HSI interface during operation (in simulation). Finally, we demonstrate live operation of real robots.

Robotic fleets can be extremely efficient when working concurrently and collaboratively, e.g., for delivery, surveillance, search and rescue. However, it can be demanding or even impractical for an operator to directly control each robot. Thus, autonomy of the fleet and its online interaction with the operator are both essential, particularly in dynamic and partially unknown environments. The operator might need to add new tasks, cancel some tasks, change priorities and modify planning results. How to design the procedure for these interactions and efficient algorithms to fulfill these needs have been mostly neglected in the related literature. Thus, this work proposes a human-centric coordination and supervision scheme (HECTOR) for large-scale robotic fleets under continual and uncertain temporal tasks. It consists of three hierarchical layers: (I) the bidirectional and multimodal protocol of online human-fleet interaction, where the operator interacts with and supervises the whole fleet; (II) the rolling assignment of currently-known tasks to teams within a certain horizon, and (III) the dynamic coordination within a team given the detected subtasks during online execution. The overall mission can be as general as temporal logic formulas over collaborative actions. Such hierarchical structure allows human interaction and supervision at different granularities and triggering conditions, to both improve computational efficiency and reduce human effort. Extensive human-in-the-loop simulations are performed over heterogeneous fleets under various temporal tasks and environmental uncertainties.

Moving robot swarms from simulation into the physical world can be difficult because small discrepancies in hardware, timing and communication can cascade across interacting robots and alter group behaviour. This challenge is known as the reality gap. When swarms operate in human environments, a second challenge emerges: a perceptual gap between what the swarm physically does and what human onlookers actually perceive. This study examines the same decentralised coordination code deployed across three robotic swarms-tabletop, aerial and human-scale mobile manipulators-during live artistic performances. We find that while the underlying algorithms executed reliably on all three platforms, physical embodiment drastically altered visible motion-distorting execution timescales by more than a factor of three and reshaping trajectory geometry. More importantly, audience surveys revealed that human interpretation does not mirror technical success: how observers attribute meaning (perceived purpose) and how they react emotionally (comfort versus threat) depend heavily on physical form. Meaning attribution and emotional responses did not vary together. Our results demonstrate that successfully transferring swarm software to physical hardware is insufficient for real-world deployment. Addressing the reality gap does not guarantee that the resulting collective motion will be readily interpreted by human observers. Swarms destined for human environments must be engineered not only for decentralised stability but also for the expressive physical dynamics through which humans interpret collective motion.