While reinforcement learning (RL) achieves tremendous success in sequential decision-
making problems of many domains, it still faces key challenges of data inefficiency and the …

Behavioral cloning reduces policy learning to supervised learning by training a
discriminative model to predict expert actions given observations. Such discriminative …

In this article, we investigate the role of causal reasoning in robotics research. Inspired by a
categorization of human causal cognition, we propose a categorization of robot causal …

Artificial Intelligence (AI) is known to be a driving force behind the Industry 4.0. Nowadays
the current hype on development and industrial adoption of the AI systems is mostly …

While the concept of a conscious machine is intriguing, producing such a machine remains
controversial and challenging. Here, we describe how our work on creating a humanoid …

Manually programming robots is difficult, impeding more widespread use of robotic systems.
In response, efforts are being made to develop robots that use imitation learning. With such …

Examining complex cognitive-motor performance in humanoid robots and humans can
inform their interactions in a social context of team dynamics. Namely, the understanding of …

In the not so distant future, we can expect a world where humans and robots coexist and
interact with each other. For this to occur, we need to understand human traits, such as …

Causality is relatively unexplored in robotics even if it is highly relevant, in several respects.
In this paper, we study how a robot's causal understanding can be improved by allowing the …

The examination of neural resource allocation during complex action sequence execution is
critical to understanding human behavior. While physical systems are usually used for such …