Reliably Arranging Objects in Uncertain Domains
Reliably Arranging Objects in Uncertain Domains
复制标题
在不确定的域中可靠地排列对象
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Tomas Lozano
中科院分区：
文献类型：
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作者：
Ariel S. Anders;L. Kaelbling;Tomas Lozano
机器人技术中的一个关键挑战是尽管有传感和控制不确定性，但仍取得了可靠的结果。在这项工作中，我们探讨了机器人操纵的一致规划方法。特别是，我们解决了同时推动多个平面对象以实现指定布置而无需外部感应的问题。一致计划是一个信仰国家计划问题。信念状态是世界所有可能状态的集合，目标是找到一系列将最初信仰状态带入目标信仰状态的行动。为了进行前瞻性信念状态计划，我们基于离线物理模拟从监督学习中创建了一个确定的信念状态过渡模型。我们将我们的方法与基于物理的操纵方法进行比较，并在模拟实验中显示出明显减少的计划时间和鲁棒性。最后，我们证明了这种方法在模拟和物理机器人实验中的成功。
A crucial challenge in robotics is achieving reliable results in spite of sensing and control uncertainty. In this work, we explore the conformant planning approach to robot manipulation. In particular, we tackle the problem of pushing multiple planar objects simultaneously to achieve a specified arrangement without external sensing. Conformant planning is a belief-state planning problem. A belief state is the set of all possible states of the world, and the goal is to find a sequence of actions that will bring an initial belief state to a goal belief state. To do forward belief-state planning, we created a deterministic belief-state transition model from supervised learning based on off-line physics simulations. We compare our method with an on-line physics-based manipulation approach and show significantly reduced planning times and increased robustness in simulated experiments. Finally, we demonstrate the success of this approach in simulations and physical robot experiments.