Finite-Horizon Synthesis for Probabilistic Manipulation Domains
Finite-Horizon Synthesis for Probabilistic Manipulation Domains
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DOI：
10.1109/icra48506.2021.9561297
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发表时间：
2021-05
期刊：
影响因子：
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通讯作者：
Andrew M. Wells;Zachary K. Kingston;Morteza Lahijanian;L. Kavraki;Moshe Y. Vardi
中科院分区：
文献类型：
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作者：
Andrew M. Wells;Zachary K. Kingston;Morteza Lahijanian;L. Kavraki;Moshe Y. Vardi
机器人已经开始在工业和社会环境中与人类合作。这种合作带来了挑战：机器人必须在考虑人类行动的同时进行规划。在以前的工作中，这个问题是作为一个2人确定性游戏提出的，有有限数量的人类行动。对人类行动的限制是不直观的，在许多情况下，决定论是不可取的。在本文中，我们提出了一种新的规划方法，通过概率综合的人-机器人协同操作任务。我们引入了一个概率操纵域，它通过允许机器人和人类的动作来捕获交互，这些动作具有代表工作空间中对象的配置的状态。该任务是使用有限轨迹上的线性时序逻辑（LTLf）指定的。然后，我们将我们的操纵域转换为马尔可夫决策过程（MDP），并合成一个最优的政策，以满足这个MDP的规格。我们提出了两个新的贡献：概率操纵域的形式化，使我们能够应用现有的技术和这些域的不同编码的比较。我们的框架是验证物理UR 5机器人。
Robots have begun operating and collaborating with humans in industrial and social settings. This collaboration introduces challenges: the robot must plan while taking the human’s actions into account. In prior work, the problem was posed as a 2-player deterministic game, with a limited number of human moves. The limit on human moves is unintuitive, and in many settings determinism is undesirable. In this paper, we present a novel planning method for collaborative human-robot manipulation tasks via probabilistic synthesis. We introduce a probabilistic manipulation domain that captures the interaction by allowing for both robot and human actions with states that represent the configurations of the objects in the workspace. The task is specified using Linear Temporal Logic over finite traces (LTLf). We then transform our manipulation domain into a Markov Decision Process (MDP) and synthesize an optimal policy to satisfy the specification on this MDP. We present two novel contributions: a formalization of probabilistic manipulation domains allowing us to apply existing techniques and a comparison of different encodings of these domains. Our framework is validated on a physical UR5 robot.