超越任务：利用现代机器人学习技术复现类动物行为基质的愿景
Beyond Tasks: A Vision for Reproducing an Animal-like Behavioral Substrate Using Modern Robot Learning Techniques
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中文总结 AI 辅助
本文提出以行为学行为基质为视角，研究机器人在动态环境中持续保持情境化与行为连贯的能力，并倡导以机器人动物伴侣为场景探索长期互动与学习。
中文摘要 AI 辅助
近年来机器人学习的进展已产生了能力日益增强的具身智能体。然而，相对较少的关注被给予动物持续展现的一种更基本的能力形式：随着物理、环境和社会需求随时间变化，保持情境化、响应性以及行为连贯性的能力。我们提出将行为学行为基质（ethological behavioral substrate）作为研究人工智能体中这种能力形式的概念透镜。我们不主张将动物展现的这些行为视为一组孤立的技能，而是认为它们在竞争性需求下的持续协调构成了现代机器人学习的一个重要且尚未充分探索的目标。我们进一步提出，机器人动物伴侣（robotic animal companions）作为研究以人为中心环境中持续互动与适应的有用研究场景。此类系统提供了一个机会，用以调查社会行为、记忆和持续学习如何在长期互动中发展。这一视角促使进一步研究这种持久的行为能力如何补充具身智能体中的更高层次能力。
英文摘要
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.
