Scalable Learned Geometric Feasibility for Cooperative Grasp and Motion Planning
Scalable Learned Geometric Feasibility for Cooperative Grasp and Motion Planning
复制标题
用于协作抓取和运动规划的可扩展学习几何可行性
DOI：
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复制
发表时间：
2022
影响因子：
5.2
通讯作者：
Jaeheung Park
中科院分区：
文献类型：
--
作者：
Suhan Park;Hyoung Cheol Kim;Jiyeong Baek;Jaeheung Park
这封信提出了一种新的学习可行性估计，认为多模态把握把握和运动规划的构成。抓取姿态固有地具有多模态结构，即，连续和离散参数。混合整数规划（MIP）是解决这些多模态问题的一种方法。然而，搜索所有的离散参数花费相当多的时间。因此，通过从几何变量中学习每个模式的可行性，可以在给定的时间限制内有效地解决问题。抓取位姿的可行性与物体的位姿和附近障碍物有关。利用这些信息，我们介绍了学习几何可行性（LGF），它优先考虑整数搜索的MIP。LGF可扩展到多个机器人和环境，因为它使用面向对象的信息学习可行性。它已被证明，以提高解决MIP问题的时间限制内的数量，并适用于各种环境设置。
This letter proposes a novel learned feasibility estimator that considers multi-modal grasp poses for grasp and motion planning. Grasp poses inherently have multi-modal structures, that is, continuous and discrete parameters. Mixed-integer programming (MIP) is one method that solves these multi-modal problems. However, searching for all the discrete parameters costs considerable time. Therefore, by learning the feasibility of each mode from the geometric variables, the problem can be solved efficiently within a given time limit. The feasibility of grasp poses is related to the pose of the object and nearby obstacles. Utilizing this information, we introduce learned geometric feasibility (LGF), which prioritizes the integer search of MIP. LGF is scalable to multiple robots and environments because it learns the feasibility using object-oriented information. It has been demonstrated to improve the number of solved MIP problems within the time limit and to be applicable to various environmental settings.
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：
--
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
通过协同离散线索为多模式规划提供信息
DOI：
10.1109/icra40945.2020.9197545
发表时间：
2020
期刊：
2020 IEEE International Conference on Robotics and Automation
影响因子：
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作者：
Kingston, Zachary;Wells, Andrew M.;Moll, Mark;Kavraki, Lydia E.
通讯作者：
Kavraki, Lydia E.
MotionBenchMaker：生成运动规划数据集并对其进行基准测试的工具
影响因子：
5.2
作者：
Chamzas, Constantinos;Quintero-Pena, Carlos;Kingston, Zachary;Orthey, Andreas;Rakita, Daniel;Gleicher, Michael;Toussaint, Marc;Kavraki, Lydia E.
通讯作者：
Kavraki, Lydia E.