Searching for objects in occluded spaces is one of the problems robots need to solve when tackling mobile manipulation tasks. Most approaches focus only on searching for a specific object. In this paper, we use the concept of relational affordances to improve occluded object search performance. Affordances define action possibilities on an object in the environment and play a role in basic cognitive capabilities. Relational affordances extend this concept by modelling relations between multiple objects. By learning and using a relational affordance model we can search for any of the multiple objects that afford a given action, each object type having a probability distribution over possible sizes and shapes, and where spatial relations between objects such as co-occurrence and stacking are modelled. The experimental results show the viability of the relational affordance models for occluded object search.

Affordances are used in robotics to model action opportunities of a robotic manipulator on an object in the environment. Previous work has shown how statistical relational learning can be used in a discrete setting to extend affordances to model relations and interactions between multiple objects being manipulated by a robotic arm and deal with environment uncertainty. In this paper, we first extend this concept of relational affordances to a continuous setting and then to a two-arm robot. A relational affordance model can first be learnt for one arm through a behavioural babbling stage, and then with the use of statistical relational learning, after constructing a symmetrical model for the other arm, two-arm manipulation actions can be modelled, where the arms can act sequentially or simultaneously. The model is evaluated in a two-arm action recognition task in a shelf object manipulation setting.

In this paper we employ probabilistic relational affordance models in a robotic manipulation task. Such affordance models capture the interdependencies between properties of multiple objects, executed actions, and effects of those actions on objects. Recently it was shown how to learn such models from observed video demonstrations of actions manipulating several objects. This paper extends that work and employs those models for sequential tasks. Our approach consists of two parts. First, we employ affordance models sequentially in order to recognize the individual actions making up a demonstrated sequential skill or high level concept. Second, we utilize the models of concepts to plan a suitable course of action to replicate the observed consequences of a demonstration. For this we adopt the framework of relational Markov decision processes. Empirical results show the viability of the affordance models for sequential manipulation skills for object placement.

Affordances define the action possibilities on an object in the environment and in robotics they play a role in basic cognitive capabilities. Previous works have focused on affordance models for just one object even though in many scenarios they are defined by configurations of multiple objects that interact with each other. We employ recent advances in statistical relational learning to learn affordance models in such cases. Our models generalize over objects and can deal effectively with uncertainty. Two-object interaction models are learned from robotic interaction with the objects in the world and employed in situations with arbitrary numbers of objects. We illustrate these ideas with experimental results of an action recognition task where a robot manipulates objects on a shelf.