The manipulation of articulated objects is of primary importance in Robotics,
and can be considered as one of the most complex manipulation tasks.
Traditionally, this problem has been tackled by developing ad-hoc approaches,
which lack flexibility and portability.
In this paper we present a framework based on Answer Set Programming (ASP)
for the automated manipulation of articulated objects in a robot control
architecture. In particular, ASP is employed for representing the configuration
of the articulated object, for checking the consistency of such representation
in the knowledge base, and for generating the sequence of manipulation actions.
The framework is exemplified and validated on the Baxter dual-arm manipulator
in a first, simple scenario. Then, we extend such scenario to improve the
overall setup accuracy, and to introduce a few constraints in robot actions
execution to enforce their feasibility. The extended scenario entails a high
number of possible actions that can be fruitfully combined together. Therefore,
we exploit macro actions from automated planning in order to provide more
effective plans. We validate the overall framework in the extended scenario,
thereby confirming the applicability of ASP also in more realistic Robotics
settings, and showing the usefulness of macro actions for the robot-based
manipulation of articulated objects. Under consideration in Theory and Practice
of Logic Programming (TPLP).

本论文提出了基于 Answer Set Programming (ASP) 的框架，以实现对机器人控制结构中的关节式物体的自动化操纵。该框架通过 ASP 表示关节式物体的配置，并检查知识库中这种表示的一致性，并生成操纵动作序列。利用自动规划的宏动作来更有效地规划操纵关节式物体的机器人的操作，验证了该框架的可行性。

双臂机器人通过答案集编程操作关节式物体

Manipulation of Articulated Objects using Dual-arm Robots via Answer Set  Programming

StarCraft (SC) is one of the most popular and successful Real Time Strategy
(RTS) games. In recent years, SC is also widely accepted as a challenging
testbed for AI research because of its enormous state space, partially observed
information, multi-agent collaboration, and so on. With the help of annual
AIIDE and CIG competitions, a growing number of SC bots are proposed and
continuously improved. However, a large gap remains between the top-level bot
and the professional human player. One vital reason is that current SC bots
mainly rely on predefined rules to select macro actions during their games.
These rules are not scalable and efficient enough to cope with the enormous yet
partially observed state space in the game. In this paper, we propose a deep
reinforcement learning (DRL) framework to improve the selection of macro
actions. Our framework is based on the combination of the Ape-X DQN and the
Long-Short-Term-Memory (LSTM). We use this framework to build our bot, named as
LastOrder. Our evaluation, based on training against all bots from the AIIDE
2017 StarCraft AI competition set, shows that LastOrder achieves an 83% winning
rate, outperforming 26 bots in total 28 entrants.

本研究采用基于深度强化学习的方法构建了一款名为 LastOrder 的机器人，旨在提高 StarCraft 游戏中宏观行动的选择。实验表明，LastOrder 在 AIIDE'17 StarCraft AI 比赛中排名前列，证明了该方法的有效性。