Evidence from empirical literature suggests that explainable complex behaviors can be built from structured compositions of explainable component behaviors with known properties. Such component behaviors can be built to directly perceive and exploit affordances. Using six examples of recent research in legged robot locomotion, we suggest that robots can be programmed to effectively exploit affordances without developing explicit internal models of them. We use a generative framework to discuss the examples, because it helps us to separate—and thus clarify the relationship between—description of affordance exploitation from description of the internal representations used by the robot in that exploitation. Under this framework, details of the architecture and environment are related to the emergent behavior of the system via a generative explanation. For example, the specific method of information processing a robot uses might be related to the affordance the robot is designed to exploit via a formal analysis of its control policy. By considering the mutuality of the agent-environment system during robot behavior design, roboticists can thus develop robust architectures which implicitly exploit affordances. The manner of this exploitation is made explicit by a well constructed generative explanation.

经验文献的证据表明，可解释的复杂行为可以由具有已知特性的可解释的组件行为的结构化组成来构建。可以构建此类组件行为来直接感知和利用能力。使用最近在腿式机器人运动中研究的六个示例，我们建议可以对机器人进行编程，以有效利用可负担能力，而无需开发它们的明确内部模型。我们使用生成框架来讨论示例，因为它可以帮助我们将对负担能力开发的描述与该行为中机器人所使用的内部表示的描述分开，从而澄清它们之间的关系。在这个框架下 通过生成的解释，体系结构和环境的详细信息与系统的紧急行为相关。例如，机器人使用的信息处理的特定方法可能与通过对其控制策略进行形式化分析而设计的机器人的能力有关。通过在机器人行为设计过程中考虑代理程序-环境系统的相互关系，机器人专家可以开发出健壮的体系结构，这些体系结构隐含地利用了能力。通过充分构造的生成解释，明确了这种开发方式。通过在机器人行为设计过程中考虑代理程序-环境系统的相互关系，机器人专家可以开发出健壮的体系结构，这些体系结构隐含地利用了能力。通过充分构造的生成解释，明确了这种开发方式。通过在机器人行为设计过程中考虑代理程序-环境系统的相互关系，机器人专家可以开发出健壮的体系结构，这些体系结构隐含地利用了能力。通过充分构造的生成解释，明确了这种开发方式。