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 …

本文通过扩展TAM 技术接受模型探索高校图书馆信息共享空间中用户交互学习行为的影响因素.
在结合高校图书馆信息共享空间具体环境的基础上, 运用结构方程模型方法进行研究. 研究发现 …

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 …

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 …

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 …

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 …