This paper presents a method to associate meanings to words in manipulation tasks. We base our model on an affordance network, i.e., a mapping between robot actions, robot perceptions and the perceived effects of these actions upon objects. This knowledge is acquired by the robot in an unsupervised way by self-interaction with the environment. When a human user is involved in the process and de...

Complex cognitive processes corresponding to human control behaviors cannot be easily inferred using (1) a logical rule-based model, (2) a statistical model, or (3) an analytical predictive model. Predicting human behaviors in complex and uncertain environments like emergency evacuation is considered almost impossible (at least NP hard) in systems theory. In this paper, we explore simulating hu...

We present a novel method for learning and predicting the affordances of an object based on its physical and visual attributes. Affordance prediction is a key task in autonomous robot learning, as it allows a robot to reason about the actions it can perform in order to accomplish its goals. Previous approaches to affordance prediction have either learned direct mappings from visual features to ...

Affordance learning has, in recent years, been generating heightened interest in both the cognitive vision and developmental robotics communities. In this paper we describe the development of a system that uses a robotic arm to interact with household objects on a table surface while observing the interactions using camera systems. Various computer vision methods are used to derive, firstly, ob...

Imitation is not only a powerful means to drastically downsize the exploration space when learning behavior. It also helps to align the learning efforts of a robot group towards a common goal. However, one prerequisite in imitation, the decision of which robot to imitate, is often factored out in current research. In our work we address this question by providing a means to measure the similari...

In our targeted scenario, humans can flexibly establish joint object reference with a robot entirely on the basis of their own intuitions. To reach this aim, the robot needs to be equipped with the kind of knowledge that can be matched in a cognitively adequate way to users’ intuitive conceptual and linguistic preferences. Such an endeavour requires knowledge about human spatial object referenc...

This article summarizes the objectives and the program of the Dagstuhl seminar 06231, “Towards Affordance-based Robot Control”. It was held from June 5 to June 9, 2006, at the International Conference and Research Center for Computer Science Schloss Dagstuhl near Wadern, Germany.