kPAM-SC: Generalizable Manipulation Planning using KeyPoint Affordance and Shape Completion
kPAM-SC: Generalizable Manipulation Planning using KeyPoint Affordance and Shape Completion
复制标题
kPAM-SC：使用关键点可供性和形状完成的通用操作规划
DOI：
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复制
发表时间：
2019
期刊：
影响因子：
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通讯作者：
Russ Tedrake
中科院分区：
文献类型：
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作者：
Wei Gao;Russ Tedrake
虽然传统的操作规划方法假设已知的对象模板，但最近的“类别级操作”方法旨在操作具有潜在未知实例和大类别内形状变化的类别对象。在本文中，我们探索了一种对象表示，以实现精确的类别级操作，捕获对象配置和范围的概念，同时可推广到新的实例。基于我们之前的工作，kPAM 1，我们将语义关键点与密集几何（点云或网格）结合起来，作为感知模块和运动规划器之间的接口。利用基于学习的关键点检测和形状补全技术的进步，密集几何和关键点都可以从原始传感器输入中感知到。利用所提出的混合对象表示，我们将操作任务表述为一个运动规划问题，该问题编码了一类对象的目标配置和物理可行性。通过这种方式，许多现有的操作计划可以推广到对象的类别，并且由此产生的感知到动作的操作管道对类别内形状的大变化具有鲁棒性。大量的硬件实验表明，我们的流水线可以产生机器人轨迹，完成从未见过的物体的任务。视频演示可以在这个链接上找到：https://sites.google.com/view/generalizable-manipulation。
While traditional approaches to manipulation planning assume known object templates, recent approaches to "category-level manipulation" aim to manipulate a category of objects with potentially unknown instances and large intra-category shape variation. In this paper we explore an object representation to enable precise category-level manipulation, capturing a notion of the object configuration and extent, while being generalizable to novel instances. Building on our previous work, kPAM 1, we combine semantic keypoints with dense geometry (a point cloud or mesh) as the interface between the perception module and motion planner. Leveraging advances in learning-based keypoint detection and shape completion, both dense geometry and keypoints can be perceived from raw sensor input. Using the proposed hybrid object representation, we formulate the manipulation task as a motion planning problem which encodes both the object target configuration and physical feasibility for a category of objects. In this way, many existing manipulation planners can be generalized to categories of objects, and the resulting perception-to-action manipulation pipeline is robust to large intra-category shape variation. Extensive hardware experiments demonstrate our pipeline can produce robot trajectories that accomplish tasks with never-before-seen objects. The video demo is available on this link: https://sites.google.com/view/generalizable-manipulation.
无需几何对象模型即可拾取和放置
DOI：
10.1109/icra.2018.8460553
发表时间：
2018
期刊：
Proceedings of 2018 IEEE International Conference on Robotics and Automation (ICRA
影响因子：
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作者：
Gualtieri, Marcus;Pas, Andreas ten;Platt, Robert
通讯作者：
Platt, Robert
DOI：
10.1109/icra40945.2020.9196981
发表时间：
2019-10
期刊：
2020 IEEE International Conference on Robotics and Automation (ICRA)
影响因子：
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作者：
Mark Van der Merwe;Qingkai Lu;Balakumar Sundaralingam;Martin Matak;Tucker Hermans
通讯作者：
Mark Van der Merwe;Qingkai Lu;Balakumar Sundaralingam;Martin Matak;Tucker Hermans
DOI：
10.1109/cvpr.2019.00275
发表时间：
2019-01
期刊：
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
影响因子：
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作者：
He Wang;Srinath Sridhar;Jingwei Huang;Julien P. C. Valentin;Shuran Song;L. Guibas
通讯作者：
He Wang;Srinath Sridhar;Jingwei Huang;Julien P. C. Valentin;Shuran Song;L. Guibas