Swarm of One: Bottom-up Emergence of Stable Robot Bodies from Identical Cells (2306.12629v1)

Abstract: Unlike most human-engineered systems, biological systems are emergent from low-level interactions, allowing much broader diversity and superior adaptation to the complex environments. Inspired by the process of morphogenesis in nature, a bottom-up design approach for robot morphology is proposed to treat a robot's body as an emergent response to underlying processes rather than a predefined shape. This paper presents Loopy, a "Swarm-of-One" polymorphic robot testbed that can be viewed simultaneously as a robotic swarm and a single robot. Loopy's shape is determined jointly by self-organization and morphological computing using physically linked homogeneous cells. Experimental results show that Loopy can form symmetric shapes consisting of lobes. Using the the same set of parameters, even small amounts of initial noise can change the number of lobes formed. However, once in a stable configuration, Loopy has an "inertia" to transfiguring in response to dynamic parameters. By making the connections among self-organization, morphological computing, and robot design, this paper lays the foundation for more adaptable robot designs in the future.

• The paper introduces a bottom-up design approach where decentralized, identical cell units autonomously self-organize into coherent robot bodies.
• The methodology employs reaction-diffusion equations, notably the Fitzhugh-Nagumo model, to simulate activator-inhibitor dynamics for emergent morphology.
• Experimental results demonstrate robust formation of symmetrical, energy-efficient configurations, underscoring the potential for adaptive robotics in dynamic environments.

Bottom-Up Emergence of Robot Morphologies in "Swarm of One"

The paper introduces an innovative approach to robot design by leveraging principles of morphogenesis, specifically a bottom-up methodology that facilitates the emergence of robot shapes as distinct from traditional methods that follow a top-down orchestration in design. The research presents "Loopy," an exemplary robotic platform that epitomizes the concept of a "Swarm-of-One," where a multitude of identical, physically linked robotic cells self-organize to constitute a singular, coherent robot entity. This paper delineates the processes and outcomes connected with designing robot morphology through decentralized interactions among homogeneous agents.

Methodology and Theoretical Framework

The design philosophy underlying this investigation draws heavily from natural scientific principles, particularly morphogenesis — the process by which cellular patterns and structures self-assemble in biological organisms. The "Swarm-of-One" comes to fruition through a sequence of high-level procedures where each cell, analogized to a biological cell, functions independently yet cooperatively to execute localized decisions and interactions. The key insight is the design interpretation where the robot's morphology is seen as an emergent phenomenon arising from the interactions of its components rather than from explicit preprogramming.

Central to achieving this adaptable morphology is the application of reaction-diffusion equations, modeled on Turing’s framework, which governs the morphogen concentrations within these robotic cells. The Fitzhugh-Nagumo equations are employed to manage the dynamics of activator and inhibitor morphogens, facilitating a spontaneous and stable formation of sinusoidal distributions which consequently lead to the emergence of diverse robot shapes.

Experimental Results and Analysis

The experimental discourse outlined focuses on two primary facets: synthesis of stable emergent body shapes from varied initial conditions and temporal morphogenic adaptation in response to parameter fluctuation within the reaction-diffusion model. The empirical analysis conveys that Loopy effectively forms multiple symmetrical formations, contingent on initial noise and parameter values such as diffusion ratios and reaction rates. Notably, the robust formation of lobe structures under varying conditions attests to the stability and adaptability of this bottom-up approach.

A notable finding is the emergence of hysteresis or "inertia" in these systems, characterized by Loopy’s resistance to morphological change once a stable configuration is achieved. This behavior has potential implications for energy-efficient operations in dynamic environments, as the system does not substantially react to minor perturbations in its governing conditions.

Implications and Future Directions

The contribution of this paper lies in setting a foundational precedent for bottom-up robotic design methodologies that could lead to highly adaptable swarms and individual robots capable of significant environmental adaptation. By demonstrating that robot morphologies can be emergent, dynamic, and subject to locally governed laws akin to biological morphogenesis, the paper opens pathways to novel robotic applications, particularly where environmental adaptability and morphological flexibility are paramount.

Future research trajectories are speculated to involve the integration of sensor systems to influence morphogen distribution further, allowing robots to adapt to environmental stimuli in real-time, drawing analogs with plant root systems. Additionally, the exploration of joint emergent morphology and behavior generation, as opposed to discrete design processes, represents a compelling area for subsequent exploration, potentially enabling robots that can autonomously alter both form and function in real-time adaptive contexts.

In summary, the paper elucidates a shift towards a novel paradigm of intelligent robotic design, where emergence and self-organization supplant prescripted design, opening the floor to a rich vein of adaptable robotic systems poised to challenge the rigidity of existing models.