Affordances, a concept rooted in ecological psychology and pioneered by James J. Gibson, have emerged as a fundamental framework for understanding the dynamic relationship between individuals and their environments. Expanding beyond traditional perceptual and cognitive paradigms, affordances represent the inherent effect and action possibilities that objects offer to the agents within a given context. As a theoretical lens, affordances bridge the gap between effect and action, providing a nuanced understanding of the connections between agents' actions on entities and the effect of these actions. In this study, we propose a model that unifies object, action and effect into a single latent representation in a common latent space that is shared between all affordances that we call the affordance space. Using this affordance space, our system is able to generate effect trajectories when action and object are given and is able to generate action trajectories when effect trajectories and objects are given. In the experiments, we showed that our model does not learn the behavior of each object but it learns the affordance relations shared by the objects that we call equivalences. In addition to simulated experiments, we showed that our model can be used for direct imitation in real world cases. We also propose affordances as a base for Cross Embodiment transfer to link the actions of different robots. Finally, we introduce selective loss as a solution that allows valid outputs to be generated for indeterministic model inputs.

翻译：功能可供性（Affordances）——这一源于生态心理学并由詹姆斯·J·吉布森开创的概念，已成为理解个体与环境的动态关系的基础框架。超越传统感知与认知范式，功能可供性代表物体在特定语境中为行动者提供的固有效应与行动可能性。作为理论透镜，功能可供性弥合了效应与行动之间的鸿沟，为理解行动者对实体的操作及其效应之间的关联提供了细致入微的视角。本研究提出一种模型，该模型将物体、行动与效应统一编码为潜在空间中的单一表征——这一共享空间被称为"可供性空间"。基于该空间，系统可在给定行动与物体时生成效应轨迹，亦可在给定效应轨迹与物体时生成行动轨迹。实验表明，模型并非学习每个物体的独立行为，而是习得物体间共享的可供性关系——我们称之为"等价关系"。除仿真实验外，我们验证了该模型可直接应用于现实场景中的模仿任务。进一步地，我们提出将可供性作为跨具身迁移（Cross Embodiment transfer）的基础，用于连接不同机器人行动。最后，我们引入选择性损失（selective loss）机制，以应对不确定性模型输入的有效输出生成问题。