Hierarchical Planning for Long-Horizon Manipulation with Geometric and Symbolic Scene Graphs
Hierarchical Planning for Long-Horizon Manipulation with Geometric and Symbolic Scene Graphs
复制标题
使用几何和符号场景图进行长视野操作的分层规划
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Yuke Zhu
中科院分区：
文献类型：
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作者：
Yifeng Zhu;Jonathan Tremblay;Stan Birchfield;Yuke Zhu
提出了一种基于视觉的分层规划算法。我们的算法提供了一个神经符号任务规划和基于指定目标的低级运动生成的联合框架。该方法的核心是两级场景图表示，即几何场景图和符号场景图。这种分层表示作为操作场景的结构化、以对象为中心的抽象。我们的模型使用图神经网络来处理这些场景图，以预测高级任务计划和低级动作。我们证明了我们的方法适用于长期任务，并且可以很好地推广到新的任务目标。我们在物理模拟和现实世界的厨房存储任务中验证了我们的方法。实验表明，该方法在真实机器人上的成功率超过70%，子目标完成率接近90%，计算时间比标准的基于搜索的任务运动规划器提高了4个数量级。1
We present a visually grounded hierarchical planning algorithm for long-horizon manipulation tasks. Our algorithm offers a joint framework of neuro-symbolic task planning and low-level motion generation conditioned on the specified goal. At the core of our approach is a two-level scene graph representation, namely geometric scene graph and symbolic scene graph. This hierarchical representation serves as a structured, object-centric abstraction of manipulation scenes. Our model uses graph neural networks to process these scene graphs for predicting high-level task plans and low-level motions. We demonstrate that our method scales to long-horizon tasks and generalizes well to novel task goals. We validate our method in a kitchen storage task in both physical simulation and the real world. Experiments show that our method achieves over 70% success rate and nearly 90% of subgoal completion rate on the real robot while being four orders of magnitude faster in computation time compared to standard search-based task-and-motion planner. 1