Articulated Animal AI: An Environment for Animal-like Cognition in a Limbed Agent

Autor:	Lucas, Jeremy, Prémont-Schwarz, Isabeau
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Robotics
Druh dokumentu:	Working Paper
Popis:	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. Comment: 8 pages, accepted to Workshop on Open-World Agents (OWA-2024) at NeurIPS 2024 in Vancouver, Canada
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2410.09275 Zobrazit plný text záznamu View this record from Arxiv