Invariant Feature Mappings for Generalizing Affordance Understanding Using Regularized Metric Learning
This work addresses the challenge of sensory-grounded semantics for affordance understanding in robotics, which could improve manipulation efficiency and transfer learning, but it appears incremental as it builds on existing metric learning methods.
The paper tackles the problem of learning invariant features for object affordance understanding in robotics by using a regularized metric learning algorithm to map objects with the same affordances closer in feature space, enabling abstraction and reasoning about affordance similarities.
This paper presents an approach for learning invariant features for object affordance understanding. One of the major problems for a robotic agent acquiring a deeper understanding of affordances is finding sensory-grounded semantics. Being able to understand what in the representation of an object makes the object afford an action opens up for more efficient manipulation, interchange of objects that visually might not be similar, transfer learning, and robot to human communication. Our approach uses a metric learning algorithm that learns a feature transform that encourages objects that affords the same action to be close in the feature space. We regularize the learning, such that we penalize irrelevant features, allowing the agent to link what in the sensory input caused the object to afford the action. From this, we show how the agent can abstract the affordance and reason about the similarity between different affordances.