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.

我们提出了一个模型，将对象、动作和效果统一为单一的潜在表示，在共享的感知空间中建立了被称为行动潜力空间的各种行为潜能。使用这个行动潜力空间，我们的系统能够在给定动作和对象时生成效果轨迹，并且能够在给定效果轨迹和对象时生成动作轨迹。