Visual Robot Task Planning
Visual Robot Task Planning
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DOI：
10.1109/icra.2019.8793736
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发表时间：
2018-03
期刊：
影响因子：
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通讯作者：
Chris Paxton;Yotam Barnoy;Kapil D. Katyal;R. Arora;Gregory Hager
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文献类型：
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作者：
Chris Paxton;Yotam Barnoy;Kapil D. Katyal;R. Arora;Gregory Hager
探索是解决新环境中具有挑战性的问题的关键，但它尚未被深入探索应用于感知驱动机器人的任务规划。我们提出视觉机器人任务规划，其中我们接收输入图像，并且必须生成一系列高级动作和相关观察来实现某些任务。在本文中，我们描述了一种神经网络架构和相关的规划算法，该算法（1）学习可以生成预期未来的世界表示，（2）使用该生成模型来模拟各种环境中高级操作序列的结果，以及（3）通过蒙特卡罗树搜索的变体评估这些操作，以找到特定问题的可行解决方案。我们的方法使我们能够可视化中间运动目标，并学习从视觉信息中规划复杂的活动，并使用它来生成和可视化块堆叠模拟的保留示例的任务计划。
Prospection is key to solving challenging problems in new environments, but it has not been deeply explored as applied to task planning for perception-driven robotics. We propose visual robot task planning, where we take in an input image and must generate a sequence of high-level actions and associated observations that achieve some task. In this paper, we describe a neural network architecture and associated planning algorithm that (1) learns a representation of the world that can generate prospective futures, (2) uses this generative model to simulate the result of sequences of high-level actions in a variety of environments, and (3) evaluates these actions via a variant of Monte Carlo Tree Search to find a viable solution to a particular problem. Our approach allows us to visualize intermediate motion goals and learn to plan complex activity from visual information, and used this to generate and visualize task plans on held-out examples of a block-stacking simulation.