外观
论文arXiv 2609.331651 个月前
超越任务：使用现代机器人学习技术复现类动物行为基底的愿景
原文链接：Beyond Tasks: A Vision for Reproducing an Animal-like Behavioral Substrate Using Modern Robot Learning Techniques（arXiv 2609.33165 · 发布日期：2026-09-27）
Beyond Tasks: A Vision for Reproducing an Animal-like Behavioral Substrate Using Modern Robot Learning Techniques
提出以动物行为基底为视角，探讨现代机器人学习如何在机器人动物伴侣中研究持续交互与适应。
核心要点
- 现有机器人学习较少关注动物持续展现的情境化、响应性和行为连贯能力。
- 论文主张将竞争需求下的持续行为协调视为行为基底，而非孤立技能集合。
- 提出机器人动物伴侣作为研究人本环境中持续交互与适应的有用场景。
- 该视角关注长期交互中社会行为、记忆和持续学习如何发展。
验证范围与局限：摘要未报告实验验证，主要提出概念视角和研究设定。
英文摘要 (Abstract)
Recent advances in robot learning have produced increasingly capable embodied agents. Yet comparatively less attention has been given to a more basic form of competence that animals exhibit continuously: the ability to remain situated, responsive, and behaviorally coherent as physical, environmental, and social demands change over time. We propose the ethological behavioral substrate as a conceptual lens for studying this form of competence in artificial agents. Rather than treating these behaviors that animals exhibit as a set of isolated skills, we argue that their continual coordination under competing demands constitutes an important and underexplored target for modern robot learning. We further propose robotic animal companions as a useful research setting for studying sustained interaction and adaptation in human-centered environments. Such systems provide an opportunity to investigate how social behavior, memory, and continual learning develop over long periods of interaction. This perspective motivates further investigation of how such persistent behavioral competence may complement higher-level capabilities in embodied agents.
作者：Samiyuru Menik, Hemadri Jayalath
来源
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