论文标题
POMDP操纵计划在对象组成不确定性下
POMDP Manipulation Planning under Object Composition Uncertainty
论文作者
论文摘要
在混乱的环境中操纵未知对象很困难，因为将场景分割成对象，即对象组成是不确定的。由于这种不确定性，较早的工作集中在识别“最佳”对象组成并相应地决定操纵操作，或者试图贪婪地收集有关“最佳”对象组成的信息。与较早的工作相反，我们1）在计划中利用不同可能的对象组成，2）利用机器人动作提供的对象组成信息，3）考虑到不同竞争对象假设对要执行的实际任务的影响。我们将操纵计划问题视为可观察到的马尔可夫决策过程（POMDP），该过程计划了对象组成的可能假设。 POMDP模型选择了最大化长期预期任务特定任务实用程序的动作，并且在这样做的同时，考虑了信息性措施的价值以及不同对象假设对完成任务的影响。在模拟和使用RGB-D传感器，Kinova Jaco和Franka Emika Panda机器人臂的实验中，一种概率方法优于仅考虑最可能的物体组成和长期计划的方法，超过了贪婪的决策。
Manipulating unknown objects in a cluttered environment is difficult because segmentation of the scene into objects, that is, object composition is uncertain. Due to this uncertainty, earlier work has concentrated on either identifying the "best" object composition and deciding on manipulation actions accordingly, or, tried to greedily gather information about the "best" object composition. Contrary to earlier work, we 1) utilize different possible object compositions in planning, 2) take advantage of object composition information provided by robot actions, 3) take into account the effect of different competing object hypotheses on the actual task to be performed. We cast the manipulation planning problem as a partially observable Markov decision process (POMDP) which plans over possible hypotheses of object compositions. The POMDP model chooses the action that maximizes the long-term expected task specific utility, and while doing so, considers the value of informative actions and the effect of different object hypotheses on the completion of the task. In simulations and in experiments with an RGB-D sensor, a Kinova Jaco and a Franka Emika Panda robot arm, a probabilistic approach outperforms an approach that only considers the most likely object composition and long term planning outperforms greedy decision making.
