Population Dynamics in ARIEL Robotics Systems Featuring Embodied Evolution via Spatial Mating Mechanisms

• URL: http://arxiv.org/abs/2604.26822v1
• Date: Wed, 29 Apr 2026 15:48:53 GMT
• Title: Population Dynamics in ARIEL Robotics Systems Featuring Embodied Evolution via Spatial Mating Mechanisms
• Abstract summary: We present a Spatially Embedded Evolutionary Algorithm where robot individuals exist in a physically simulated 2D environment, must navigate to encounter potential mates, and compete for survival under various spatially-aware selection pressures.<n>Using HyperNEAT evolved neural controllers for ARIEL-inspired quadrupeds in MuJoCo, we investigate how structure fundamentally alters evolutionary dynamics.
• Score: 0.14337588659482522
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a Spatially Embedded Evolutionary Algorithm where robot individuals exist in a physically simulated 2D environment, must navigate to encounter potential mates, and compete for survival under various spatially-aware selection pressures. Using HyperNEAT evolved neural controllers for ARIEL gecko-inspired quadrupeds in MuJoCo, we investigate how spatial structure fundamentally alters evolutionary dynamics. Our experiments show a modest 4.9% difference in peak fitness between proximity-based and random pairing possibly within stochastic variation while combining spatial parent selection with stochastic death selection produces unstable population dynamics. We discover a continuous phase transition in energy-based selection experiments, with critical zone count separating extinction-dominated and explosion-dominated regimes. Our density-dependent death selection mechanism achieves 97% completion rates but causes fitness decline, revealing a fundamental dilemma where decoupled mechanisms produce bistable dynamics, positively coupled mechanisms create counter-selection pressures, and only deterministic fitness-based selection maintains stability. These findings provide important constraints for future spatial EA design.

Related papers

• Learning Context-Aware Neural ODE Dynamics for Adaptive Robotic Control [55.01272784312405]
  We propose a context-aware dynamics model based on neural ordinary differential equations.<n>We validate the approach across diverse robotic platforms, including a quadrotor in simulation, as well as a Sphero BOLT robot and a Fanuc manipulator in real-world experiments.
  arXiv  Detail & Related papers  (2026-06-13T20:55:46Z)
• Learn Weightlessness: Imitate Non-Self-Stabilizing Motions on Humanoid Robot [56.377161933570335]
  Humans naturally exploit a "weightless" state during non-self-stabilizing (NSS) motions.<n>Inspired by this biological mechanism, we propose a weightlessness-state auto-labeling strategy for dataset annotation.<n>Our work bridges the gap between precise trajectory tracking and adaptive environmental interaction, offering a biologically-inspired solution for contact-rich humanoid control.
  arXiv  Detail & Related papers  (2026-04-23T07:10:05Z)
• Evolving the Complete Muscle: Efficient Morphology-Control Co-design for Musculoskeletal Locomotion [36.2932241309361]
  We focus on the morphology and control co-design of musculoskeletal systems.<n>We introduce a Complete Musculoskeletal Morphological Evolution Space that simultaneously evolves muscle strength, velocity, and stiffness.<n>Our method demonstrates superior learning efficiency and locomotion stability compared to fixed-morphology and standard evolutionary baselines.
  arXiv  Detail & Related papers  (2026-04-14T15:06:55Z)
• MIOFlow 2.0: A unified framework for inferring cellular stochastic dynamics from single cell and spatial transcriptomics data [47.97758608694957]
  We introduce Interpolating Manifold-Transport Flow (MIOFlow) 2.0.<n>This framework learns biologically informed cellular trajectories by integrating manifold learning, optimal transport, and neural differential equations.<n>It is validated on synthetic datasets, embryoid body differentiation, and spatially resolved axolotl brain regeneration.
  arXiv  Detail & Related papers  (2026-03-23T20:49:45Z)
• Evolutionary Systems Thinking -- From Equilibrium Models to Open-Ended Adaptive Dynamics [1.2691047660244335]
  Complex change is often described as "evolutionary" in economics, policy, numerically and technology.<n>This paper argues that evolutionary dynamics should be treated as a core system-thinking problem rather than as a biological metaphor.
  arXiv  Detail & Related papers  (2026-02-17T19:17:50Z)
• How Information Evolves: Stability-Driven Assembly and the Emergence of a Natural Genetic Algorithm [1.2691047660244335]
  We present StabilityDriven Assembly (SDA), a framework in which hallmark assembly combined with persistence biases populations toward longer-lived motifs.<n>We apply SDA/GA to chemical symbol space using SMILES fragments with recombination, mutation, and a stability function.<n>Results motivate an evolutionary ladder hypothesis where persistence-driven selection precedes genetic replication.
  arXiv  Detail & Related papers  (2026-01-22T15:47:48Z)
• Langevin Flows for Modeling Neural Latent Dynamics [81.81271685018284]
  We introduce LangevinFlow, a sequential Variational Auto-Encoder where the time evolution of latent variables is governed by the underdamped Langevin equation.<n>Our approach incorporates physical priors -- such as inertia, damping, a learned potential function, and forces -- to represent both autonomous and non-autonomous processes in neural systems.<n>Our method outperforms state-of-the-art baselines on synthetic neural populations generated by a Lorenz attractor.
  arXiv  Detail & Related papers  (2025-07-15T17:57:48Z)
• DARLEI: Deep Accelerated Reinforcement Learning with Evolutionary Intelligence [77.78795329701367]
  We present DARLEI, a framework that combines evolutionary algorithms with parallelized reinforcement learning. We characterize DARLEI's performance under various conditions, revealing factors impacting diversity of evolved morphologies. We hope to extend DARLEI in future work to include interactions between diverse morphologies in richer environments.
  arXiv  Detail & Related papers  (2023-12-08T16:51:10Z)
• DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models [102.13968267347553]
  We present DiffuseBot, a physics-augmented diffusion model that generates soft robot morphologies capable of excelling in a wide spectrum of tasks. We showcase a range of simulated and fabricated robots along with their capabilities.
  arXiv  Detail & Related papers  (2023-11-28T18:58:48Z)
• The scaling of goals via homeostasis: an evolutionary simulation, experiment and analysis [0.0]
  We propose that evolution pivoted the collective intelligence of cells during morphogenesis into behavioral intelligence by scaling up the goal states at the center of homeostatic processes. We found that these emergent morphogenetic agents exhibit a number of predicted features, including the use of stress propagation dynamics to achieve its target morphology. We propose that this system is a first step toward a quantitative understanding of how evolution scales minimal goal-directed behavior (homeostatic loops) into higher-level problem-solving agents in morphogenetic and other spaces.
  arXiv  Detail & Related papers  (2022-11-15T21:48:44Z)
• Neuromorphic adaptive spiking CPG towards bio-inspired locomotion of legged robots [58.720142291102135]
  Spiking Central Pattern Generator generates different locomotion patterns driven by an external stimulus. The locomotion of the end robotic platform (any-legged robot) can be adapted to the terrain by using any sensor as input.
  arXiv  Detail & Related papers  (2021-01-24T12:44:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.