This paper presents Coralai, a framework for exploring diverse ecosystems of
Neural Cellular Automata (NCA). Organisms in Coralai utilize modular,
GPU-accelerated Taichi kernels to interact, enact environmental changes, and
evolve through local survival, merging, and mutation operations implemented
with HyperNEAT and PyTorch. We provide an exploratory experiment implementing
physics inspired by slime mold behavior showcasing the emergence of competition
between sessile and mobile organisms, cycles of resource depletion and
recovery, and symbiosis between diverse organisms. We conclude by outlining
future work to discover simulation parameters through measures of multi-scale
complexity and diversity. Code for Coralai is available at
this https URL , video demos are available at
this https URL .

Coralai 是一个用于探索神经元元胞自动机（NCA）多样化生态系统的框架，通过使用模块化的 GPU 加速 Taichi 内核，以 HyperNEAT 和 PyTorch 实现的局部存活、合并和变异操作，实现了生物在 Coralai 中的相互作用、环境变化和进化。通过模拟黏菌行为的物理实验，展示了固定和移动生物之间的竞争、资源消耗和恢复循环以及多样化生物之间的共生关系。同时提出未来研究工作的设想，通过多尺度复杂性和多样性的度量来发现模拟参数。Coralai 的代码可在该 URL 获取，视频演示可在该 URL 获取。

Coralai: 具有自主进化的体验感知神经元元胞自动机生态系统

Coralai: Intrinsic Evolution of Embodied Neural Cellular Automata  Ecosystems

In recent years, the rapid advancement of Large Language Models (LLMs) such
as the Generative Pre-trained Transformer (GPT) has attracted increasing
attention due to their potential in a variety of practical applications. The
application of LLMs with Embodied Intelligence has emerged as a significant
area of focus. Among the myriad applications of LLMs, navigation tasks are
particularly noteworthy because they demand a deep understanding of the
environment and quick, accurate decision-making. LLMs can augment embodied
intelligence systems with sophisticated environmental perception and
decision-making support, leveraging their robust language and image-processing
capabilities. This article offers an exhaustive summary of the symbiosis
between LLMs and embodied intelligence with a focus on navigation. It reviews
state-of-the-art models, research methodologies, and assesses the advantages
and disadvantages of existing embodied navigation models and datasets. Finally,
the article elucidates the role of LLMs in embodied intelligence, based on
current research, and forecasts future directions in the field. A comprehensive
list of studies in this survey is available at
this https URL

近年来，大型语言模型（LLMs）如生成预训练变换器（GPT）的快速进展引起了越来越多的关注，由于它们在各种实际应用中的潜力。LLMs 与具身智能的应用已成为一个重要的研究领域，其中导航任务尤为引人注目，因为它们需要对环境有深刻的理解和快速准确的决策能力。本文全面总结了 LLMs 与具身智能之间的共生关系，重点关注导航领域。它回顾了最先进的模型、研究方法，并评估了现有具身导航模型和数据集的优缺点。最后，根据当前研究，阐明了 LLMs 在具身智能中的作用，并预测了未来的研究方向。本调查的研究清单可在此 https URL 上找到。