The goal of neural-symbolic computation is to integrate the connectionist and symbolist
paradigms. Prior methods learn the neural-symbolic models using reinforcement learning …

We address the problem of learning Hierarchical Task Networks (HTNs) from unannotated
task demonstrations, while retaining action execution preferences present in the …

National Key Laboratory of General Artificial Intelligence, Beijing Institute for General
Artificial Intelligence (BIGAI), China Retrieving out-of-reach objects is a crucial task in virtual …

Compliant robot tasks such as grinding require a robot to use a specific control strategy and
to consider a number of process parameters. It is demanding to program such behaviors …

Building a human‐centered editable world can be fully realized in a virtual environment.
Both mixed reality (MR) and virtual reality (VR) are feasible solutions to support the attribute …

We present a novel augmented reality (AR) interface to provide effective means to diagnose
a robot's erroneous behaviors, endow it with new skills, and patch its knowledge structure …

为了提高机器人学习技能的能力, 免除人工示教过程, 本文基于对无特殊标记的人类演示视频的
观察, 提出了一种基于序列到序列模式的机器人指令自动生成框架. 首先, 使用Mask R-CNN …

Human-computer interaction (HCI) plays an important role in the near-field mixed reality, in
which the hand-based interaction is one of the most widely-used interaction modes …

Abstract Reasoning about commonsense from visual input remains an important and
challenging problem in the field of computer vision. It is important because the ability to …

Artificial agents expected to operate alongside humans in daily life will be expected to
handle novel circumstances and explain their behavior to humans. In this dissertation, we …