事件预测行为基元的自主识别与目标导向调用
Autonomous Identification and Goal-Directed Invocation of Event-Predictive Behavioral Primitives
AI总结：
针对从感觉运动经验中学习组合式抽象的难题，提出SUBMODES计算架构，通过自组织神经控制器和惊讶度信号自主识别行为基元，在两类机器人上验证了学习效果，且可用于目标导向控制。
摘要：
人类的自主行为似乎由小型的基础构建模块或行为基元构成。尽管这种模块化组织对复杂运动技能的学习以及行为对新环境的灵活适应至关重要，但如何从感觉运动经验中学习有意义的组合式抽象概念仍是一项开放性挑战。本文提出一种计算学习架构，名为基于惊讶度的行为模块化事件预测结构（SUBMODES），该架构可探索行为并完全从零开始识别潜在的行为单元。SUBMODES架构利用自组织神经控制器启动感觉运动探索。在探索自身身体的行为能力过程中，系统学习能够预测感觉运动动态并生成相应行为的模块化结构。与近期的事件感知理论一致，该系统利用意外的预测误差信号（即惊讶度）来检测连续行为基元之间的转换。我们的研究表明，当应用于两个身体运动学特性完全不同的机器人系统时，该系统能够成功学习多种复杂且符合实际的行为基元。此外，经过初始的自我探索后，系统能够逐步更有效地利用其学习到的预测模型，在不同任务和环境中启动模型预测规划与目标导向控制。
英文摘要：
Voluntary behavior of humans appears to be composed of small, elementary building blocks or behavioral primitives. While this modular organization seems crucial for the learning of complex motor skills and the flexible adaption of behavior to new circumstances, the problem of learning meaningful, compositional abstractions from sensorimotor experiences remains an open challenge. Here, we introduce a computational learning architecture, termed surprise-based behavioral modularization into event-predictive structures (SUBMODES), that explores behavior and identifies the underlying behavioral units completely from scratch. The SUBMODES architecture bootstraps sensorimotor exploration using a self-organizing neural controller. While exploring the behavioral capabilities of its own body, the system learns modular structures that predict the sensorimotor dynamics and generate the associated behavior. In line with recent theories of event perception, the system uses unexpected prediction error signals, i.e., surprise, to detect transitions between successive behavioral primitives. We show that, when applied to two robotic systems with completely different body kinematics, the system manages to learn a variety of complex and realistic behavioral primitives. Moreover, after initial self-exploration the system can use its learned predictive models progressively more effectively for invoking model predictive planning and goal-directed control in different tasks and environments.
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。
