Creating autonomous robots that can actively explore the environment, acquire knowledge
and learn skills continuously is the ultimate achievement envisioned in cognitive and …

Using human tools can significantly benefit robots in many application domains. Such ability
would allow robots to solve problems that they were unable to without tools. However, robot …

We present a new method to detect object affordances in real-world scenes using deep
Convolutional Neural Networks (CNN), an object detector and dense Conditional Random …

The concept of affordances appeared in psychology during the late 60s as an alternative
perspective on the visual perception of the environment. It was revolutionary in the intuition …

JJ Gibson's concept of affordance, one of the central pillars of ecological psychology, is a
truly remarkable idea that provides a concise theory of animal perception predicated on …

Selection of appropriate tools and use of them when performing daily tasks is a critical
function for introducing robots for domestic applications. In previous studies, however …

This letter presents a deep learning framework to predict the affordances of object parts for
robotic manipulation. The framework segments affordance maps by jointly detecting and …

This letter presents a framework to detect and rank affordances of novel objects to assist with
robotic manipulation tasks. The framework segments the affordance map of unseen objects …

Affordances are key attributes of what must be perceived by an autonomous robotic agent in
order to effectively interact with novel objects. Historically, the concept derives from the …

Deep learning models often need large amounts of training samples (thousands of training
samples) to effectively extract hidden patterns in the data, thus achieving better results …