A long-standing goal of AI is to enable robots to plan in the face of uncertain and incomplete
information, and to handle task failure intelligently. This paper shows how to achieve this …

We propose Imaginet, a model of learning visually grounded representations of language
from coupled textual and visual input. The model consists of two Gated Recurrent Unit …

The presence of robots in everyday life is increasing day by day at a growing pace. Industrial
and working environments, health-care assistance in public or domestic areas can benefit …

Being able to quickly and naturally teach robots new knowledge is critical for many future
open-world human-robot interaction scenarios. In this paper we present a novel approach to …

The ability of an object classifier to adapt to new data and incorporate new classes on the fly
is of paramount importance for robots operating in the real world. This paper presents an …

We present a multi-modal dialogue system for interactive learning of perceptually grounded
word meanings from a human tutor. The system integrates an incremental, semantic …

In order to perform complex tasks in realistic human environments, robots need to be able to
learn new concepts in the wild, incrementally, and through their interactions with humans …

We present a framework capable of tackilng the problem of continual object recognition in a
setting which resembles that under which humans see and learn. This setting has a set of …

This paper describes an autonomous system for knowledge acquisition based on artificial
curiosity. The proposed approach allows a humanoid robot to discover, in an indoor …

We argue that computational modelling of perception, action, language, and cognition
introduces several requirements on a formal semantic theory and its practical …