构建可动肢体的动物认知测试环境，支持复杂行为与泛化能力评估。
Articulated Animal AI: An Environment for Animal-like Cognition in a Limbed Agent
- 引入肢体结构，模拟真实动物运动与环境交互。
- 集成课程训练与随机化测试，提升模型泛化性能。
- 适合研究动物类智能与具身认知的学者使用。
本文提出面向动物认知的可动肢体智能环境（Articulated Animal AI Environment），是此前AnimalAI环境的增强版本。主要改进包括增加代理肢体，使智能体能够实现更复杂的动作和与环境的交互，更贴近真实动物行为。测试基准包含集成式课程训练流程与评估工具，用户无需自行设计训练程序。测试与训练过程均采用随机化设计，有助于提升智能体的泛化能力。这些改进显著扩展了原始AnimalAI框架的功能，可用于多维度评估智能体在动物认知方面的表现。
原文摘要 · Abstract (English)
This paper presents the Articulated Animal AI Environment for Animal Cognition, an enhanced version of the previous AnimalAI Environment. Key improvements include the addition of agent limbs, enabling more complex behaviors and interactions with the environment that closely resemble real animal movements. The testbench features an integrated curriculum training sequence and evaluation tools, eliminating the need for users to develop their own training programs. Additionally, the tests and training procedures are randomized, which will improve the agent's generalization capabilities. These advancements significantly expand upon the original AnimalAI framework and will be used to evaluate agents on various aspects of animal cognition.
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