Swarm Body: Embodied Swarm Robots

TL;DR

This paper introduces Embodied Swarm Robots, a concept in which a swarm of small robots forms a functional, deformable body part (a hand) and can adapt in size, density, and robot-to-subgoal mappings. It presents a framework with bone-based and silhouette-based subgoal formation, static/dynamic assignment, and a local path-planning approach to achieve embodied motion, validated through VR and real-world experiments. Key findings show that bone-dynamic algorithms and sparser densities tend to enhance body ownership and agency, with fingertip representations playing a crucial role, and that the approach supports practical applications in physical telepresence, transformable forms, and haptic gesture communication. The work provides design insights and analyzes limitations and future directions, offering a new design space for robust, scalable embodied interactions using swarm robotics.

Abstract

The human brain's plasticity allows for the integration of artificial body parts into the human body. Leveraging this, embodied systems realize intuitive interactions with the environment. We introduce a novel concept: embodied swarm robots. Swarm robots constitute a collective of robots working in harmony to achieve a common objective, in our case, serving as functional body parts. Embodied swarm robots can dynamically alter their shape, density, and the correspondences between body parts and individual robots. We contribute an investigation of the influence on embodiment of swarm robot-specific factors derived from these characteristics, focusing on a hand. Our paper is the first to examine these factors through virtual reality (VR) and real-world robot studies to provide essential design considerations and applications of embodied swarm robots. Through quantitative and qualitative analysis, we identified a system configuration to achieve the embodiment of swarm robots.

Figures (19)

• Figure 1: Example of the (1) subgoal formation generation, (2) robot assignment, and (3) local path planning.
• Figure 2: Bone- (top) and silhouette-based algorithms (bottom) to obtain subgoal formations. The bone-based algorithm obtains the subgoal formation based on the hand bone positions projected on the horizontal plane. The numbers in the figure correspond to the bone IDs provided by the Quest 2 hand tracking. The silhouette-based algorithm classifies the vertices of the hand mesh projected on the horizontal plane into clusters (shown in different colors in the figure) based on the number of robots. Then, the vertices closest to each cluster centroid become the subgoal positions.
• Figure 3: The VR setup for the reaching tasks (a). The purple area is the starting area, and the green object is the hand-shaped reaching target. Participants were instructed to (b) reach the target with the specified hand sign and (c) fit the swarm robots in the green area.
• Figure 4: Hand signs used in the VR embodiment experiment. Participants make the rock, scissors, and paper shapes with their right hands, palms down. For the reversed paper sign, the palm should face up.
• Figure 5: The predetermined subgoal positions relative to the hand bones. The gray crosses are the subgoal positions.