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With the continuous expansion of robot applications from traditional industrial fields to service fields, new environments and tasks have put forward new requirements for the flexible grasping function of robotic arms.This thesis studies the methods of learning for grasping of daily objects in unstructured scenes, by conducting research on 6-DoF grasp pose detection, domain adaptation on feature learning and robot grasp viewpoint searching.The main contents of the thesis are summarized as follows:


(1) To address the issues of large search space and partial observation in 6-DoF grasp pose detection in unstructured scenes,a grasp pose detection based on voting mechanism is proposed in this thesis.Through voting mechanism, the sample space of grasp pose detection is narrowed,while local geometry feature from different regions of the point cloud is collected to achieve the generation and evaluation of grasp poses.Experiments conducted on both simulated and actual robots demonstrate that the performance of the grasp pose detection based on voting mechanism can obtain stable and practically executable grasp poses, and can effectively complete the taskof 6-DoF grasping.


(2) Aiming at the problem of performance decrease of models trained using simulated data when applied to real world,which is caused by the difference between the simulation and real robot environments,a grasp pose detection method based on domain adapted feature learning is proposed in this thesis.By an adversarial learning strategy based on point cloud fusion, the feature extractor is encouraged to extract the invariant features in the real and simulation data, allowing the grasp pose generator to process the feature information of the two domains evenly.Experimental results demonstrate that the proposed method can successfully enhance the network's performance in the target domain, and is also applicable to different network structures as well as the transference of data between diverse datasets, which demonstrated its good generalization and practicality.


(3) Aiming at the problem that the target object in a cluttered scene cannot be effectively grasped due to the partial visibility of the agent from a single viewpoint, grasp viewpoint searching approach based on grasping ability guidance is proposed in this thesis. Combining the visibility and graspability of the target object, the reward function is proposed, and the grasp view search policy is trained through reinforcement learning, to guide the active search of the optimal grasping viewpoint of the robotic arm.Robot experiments shows that this view search policy can guide the robotic arm to successfully grasp the target objects that are not visible in the initial observation after a few viewpoint optimizations, which shows the proposed method has high search efficiency and success rate of grasping.


Keyword机器人抓取 深度学习 领域自适应 虚实迁移 抓取视角搜索
Sub direction classification智能机器人
planning direction of the national heavy laboratory实体人工智能系统感认知
Paper associated data
Document Type学位论文
Recommended Citation
GB/T 7714
郑力铭. 面向非结构化场景的日常物品抓取[D],2023.
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Thesis.pdf(25181KB)学位论文 限制开放CC BY-NC-SA
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