为了让机器人获得更加通用的能力,抓取是机器人必要掌握的技能.针对目前大多数机器人抓取决策方法存在物品特征理解浅显,缺乏抓取先验知识,导致任务兼容性较差的问题,同时受大脑中分区分块功能结构的启发,提出了将物品感知、先验知识和...为了让机器人获得更加通用的能力,抓取是机器人必要掌握的技能.针对目前大多数机器人抓取决策方法存在物品特征理解浅显,缺乏抓取先验知识,导致任务兼容性较差的问题,同时受大脑中分区分块功能结构的启发,提出了将物品感知、先验知识和抓取任务融合的认知决策模型.该模型包含卷积感知网络、记忆图网络和贝叶斯决策网络三部分,分别实现了物品能供性(affordance)提取、抓取先验知识推理和联想,以及信息融合编码决策,三部分之间的信息流以语义向量的形式传递.利用UMD part affordance数据集、该文构建的抓取常识图和决策数据集对3个网络分别进行训练,认知决策模型的测试准确率达到99.8%,并且抓取位置可视化结果展示了决策的正确性.该模型还能判断物品是否属于当前任务场景,以决策是否抓取以及选择什么部位抓取物品,有助于提高机器人实际场景的应用能力.展开更多
Due to the encephalic tissues are highly irregular, three-dimensional (3D) modeling of brain always leads to compli- cated computing. In this paper, we explore an efficient method for brain surface reconstruction fr...Due to the encephalic tissues are highly irregular, three-dimensional (3D) modeling of brain always leads to compli- cated computing. In this paper, we explore an efficient method for brain surface reconstruction from magnetic reso- nance (MR) images of head, which is helpful to surgery planning and tumor localization. A heuristic algorithm is pro- posed foi" surface triangle mesh generation with preserved features, and the diagonal length is regarded as the heuristic information to optimize the shape of triangle. The experimental results show that our approach not only reduces the computational complexity, but also completes 3D visualization with good quality.展开更多
文摘为了让机器人获得更加通用的能力,抓取是机器人必要掌握的技能.针对目前大多数机器人抓取决策方法存在物品特征理解浅显,缺乏抓取先验知识,导致任务兼容性较差的问题,同时受大脑中分区分块功能结构的启发,提出了将物品感知、先验知识和抓取任务融合的认知决策模型.该模型包含卷积感知网络、记忆图网络和贝叶斯决策网络三部分,分别实现了物品能供性(affordance)提取、抓取先验知识推理和联想,以及信息融合编码决策,三部分之间的信息流以语义向量的形式传递.利用UMD part affordance数据集、该文构建的抓取常识图和决策数据集对3个网络分别进行训练,认知决策模型的测试准确率达到99.8%,并且抓取位置可视化结果展示了决策的正确性.该模型还能判断物品是否属于当前任务场景,以决策是否抓取以及选择什么部位抓取物品,有助于提高机器人实际场景的应用能力.
基金supported by the National Natural Science Foundation of China(No.61202169)
文摘Due to the encephalic tissues are highly irregular, three-dimensional (3D) modeling of brain always leads to compli- cated computing. In this paper, we explore an efficient method for brain surface reconstruction from magnetic reso- nance (MR) images of head, which is helpful to surgery planning and tumor localization. A heuristic algorithm is pro- posed foi" surface triangle mesh generation with preserved features, and the diagonal length is regarded as the heuristic information to optimize the shape of triangle. The experimental results show that our approach not only reduces the computational complexity, but also completes 3D visualization with good quality.