Deep imitation learning is a promising approach that does not require hard-coded control
rules in autonomous robot manipulation. The current applications of deep imitation learning …

Engineers have long employed control systems utilizing models and feedback loops to
control real-world systems. Limitations of model-based control led to a generation of …

This paper introduces a new approach to develop robots that can learn general affordance
relations from their experiences. Our approach is a part of larger efforts to develop a …

Despite significant improvements in contemporary machine learning, symbolic methods
currently outperform artificial neural networks on tasks that involve compositional reasoning …

To engage in human-like dialogue, robots require the ability to describe the objects,
locations, and people in their environment, a capability known as" Referring Expression …

Cognition entails those mental processes enabling understanding the current situation
through senses, experience, and thought, and supporting the acquisition of new knowledge …

This paper describes our efforts to develop a robot with robust sensorimotor intelligence
using a multiagent-based robot control architecture and a biologically inspired intelligent …

The paper proposes a new approach to mode human working memory using EEG-induced
fuzzy associativi memory and attempts to recover the encoded memor information from …

Cognitive architectures allow robots to perform their operations by drawing on a process that
aims to simulate human reasoning. This paper presents an integrated semantic artificial …

One of the fundamental prerequisites for effective collaborations between interactive
partners is the mutual sharing of the attentional focus on the same perceptual events. This is …