Articulated Animal AI：面向有肢体智能体的类动物认知环境
Articulated Animal AI: An Environment for Animal-like Cognition in a Limbed Agent
AI总结：
本文提出了Articulated Animal AI Environment，通过引入智能体肢体、集成课程训练与评估工具以及随机化测试过程，扩展了原有AnimalAI框架，以支持更复杂的类动物运动并提升智能体泛化能力，用于评估动物认知。
摘要：
本文提出了用于动物认知的Articulated Animal AI Environment，它是先前AnimalAI Environment的增强版本。主要改进包括增加了智能体肢体，从而实现更复杂的行为以及与环境的交互，这些行为和交互与真实动物的运动高度相似。该测试平台具有集成的课程训练序列和评估工具，无需用户开发自己的训练程序。此外，测试和训练过程均经过随机化处理，这将提升智能体的泛化能力。这些进展显著扩展了原有的AnimalAI框架，并将用于在动物认知的各个方面对智能体进行评估。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
