To overcome novel challenges in complex domestic environments, humanoid robots can
learn from human teachers. We propose that the capability for social interaction should be a …

This paper focuses on robotic reinforcement learning with sparse rewards for natural
language goal representations. An open problem is the sample-inefficiency that stems from …

In this paper, we present a new intrinsically motivated actor-critic algorithm for learning
continuous motor skills directly from raw visual input. Our neural architecture is composed of …

Explainable AI has become an important field of research on neural machine learning
models. However, most existing methods are designed as tools that provide expert users …

We present a novel approach for interactive auditory object analysis with a humanoid robot.
The robot elicits sensory information by physically shaking visually indistinguishable plastic …

Graspirng objects is an important capability for humanoid robots. Due to complexity of
environmental and diversity of objects, it is difficult for the robot to accurately recognize and …

Robots are usually equipped with many different sensors that need to be integrated. While
most research is focused on the integration of vision with other senses, we successfully …

In this paper, we investigate the roles that social robots can take in physical exercise with
human partners. In related work, robots or virtual intelligent agents take the role of a coach …

We present a novel, hybrid neuro-genetic visuomotor architecture for object grasping on a
humanoid robot. The approach combines the state-of-the-art object detector RetinaNet, a …

We present a follow-up study on our unified visuomotor neural model for the robotic tasks of
identifying, localizing, and grasping a target object in a scene with multiple objects. Our …