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1. MACHINE LEARNING-BASED ROBOTIC OBJECT DETECTION AND GRASPING FOR COLLABORATIVE ASSEMBLY

   Jianjing Zhang, Chuanping Liu (January 2022, Proc. 2022 International Symposium on Flexible Automation (ISFA))

   Hideki Aoyama; Keiich Shirase (Ed.)

   An integral part of information-centric smart manufacturing is the adaptation of industrial robots to complement human workers in a collaborative manner. While advancement in sensing has enabled real-time monitoring of workspace, understanding the semantic information in the workspace, such as parts and tools, remains a challenge for seamless robot integration. The resulting lack of adaptivity to perform in a dynamic workspace have limited robots to tasks with pre-defined actions. In this paper, a machine learning-based robotic object detection and grasping method is developed to improve the adaptivity of robots. Specifically, object detection based on the concept of single-shot detection (SSD) and convolutional neural network (CNN) is investigated to recognize and localize objects in the workspace. Subsequently, the extracted information from object detection, such as the type, position, and orientation of the object, is fed into a multi-layer perceptron (MLP) to generate the desired joint angles of robotic arm for proper object grasping and handover to the human worker. Network training is guided by forward kinematics of the robotic arm in a self-supervised manner to mitigate issues such as singularity in computation. The effectiveness of the developed method is validated on an eDo robotic arm in a human-robot collaborative assembly case study. 

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2. Dynamic Grasping with Reachability and Motion Awareness

   Iretiayo Akinola, Jingxi Xu (January 2021, iros)

   Grasping in dynamic environments presents a unique set of challenges. A stable and reachable grasp can become unreachable and unstable as the target object moves, motion planning needs to be adaptive and in real time, the delay in computation makes prediction necessary. In this paper, we present a dynamic grasping framework that is reachabilityaware and motion-aware. Specifically, we model the reachability space of the robot using a signed distance field which enables us to quickly screen unreachable grasps. Also, we train a neural network to predict the grasp quality conditioned on the current motion of the target. Using these as ranking functions, we quickly filter a large grasp database to a few grasps in real time. In addition, we present a seeding approach for arm motion generation that utilizes solution from previous time step. This quickly generates a new arm trajectory that is close to the previous plan and prevents fluctuation. We implement a recurrent neural network (RNN) for modelling and predicting the object motion. Our extensive experiments demonstrate the importance of each of these components and we validate our pipeline on a real robot. 

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3. A Deep Learning-Based Autonomous Robot Manipulator for Sorting Application

   https://doi.org/10.1109/IRC.2020.00055  

   Bui, Hoang-Dung; Nguyen, Hai; La, Hung Manh; Li, Shuai (November 2020, 2020 Fourth IEEE International Conference on Robotic Computing (IRC))

   null (Ed.)

   Robot manipulation and grasping mechanisms have received considerable attention in the recent past, leading to development of wide-range of industrial applications. This paper proposes the development of an autonomous robotic grasping system for object sorting application. RGB-D data is used by the robot for performing object detection, pose estimation, trajectory generation and object sorting tasks. The proposed approach can also handle grasping on certain objects chosen by users. Trained convolutional neural networks are used to perform object detection and determine the corresponding point cloud cluster of the object to be grasped. From the selected point cloud data, a grasp generator algorithm outputs potential grasps. A grasp filter then scores these potential grasps, and the highest-scored grasp will be chosen to execute on a real robot. A motion planner will generate collision-free trajectories to execute the chosen grasp. The experiments on AUBO robotic manipulator show the potentials of the proposed approach in the context of autonomous object sorting with robust and fast sorting performance. 

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4. A Deep Learning-Based Autonomous Robot Manipulator for Sorting Application

   https://doi.org/DOI 10.1109/IRC.2020.00055  

   Bui, Hoang-Dung; Nguyen, Hai; La, Hung M; Li, Shuai (November 2020, 2020 Fourth IEEE International Conference on Robotic Computing)

   null (Ed.)

   bot manipulation and grasping mechanisms have received considerable attention in the recent past, leading to development of wide-range of industrial applications. This paper proposes the development of an autonomous robotic grasping system for object sorting application. RGB-D data is used by the robot for performing object detection, pose estimation, trajectory generation and object sorting tasks. The proposed approach can also handle grasping on certain objects chosen by users. Trained convolutional neural networks are used to perform object detection and determine the corresponding point cloud cluster of the object to be grasped. From the selected point cloud data, a grasp generator algorithm outputs potential grasps. A grasp filter then scores these potential grasps, and the highest-scored grasp will be chosen to execute on a real robot. A motion planner will generate collision-free trajectories to execute the chosen grasp. The experiments on AUBO robotic manipulator show the potentials of the proposed approach in the context of autonomous object sorting with robust and fast sorting performance. 

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5. Virtual Grasping Feedback and Virtual Hand Ownership

   https://doi.org/10.1145/3343036.3343132  

   Canales, Ryan; Normoyle, Aline; Sun, Yu; Ye, Yuting; Luca, Massimiliano Di; Jörg, Sophie (September 2019, ACM Symposium on Applied Perception 2019)

   In this work, we investigate the influence of different visualizations on a manipulation task in virtual reality (VR). Without the haptic feedback of the real world, grasping in VR might result in intersections with virtual objects. As people are highly sensitive when it comes to perceiving collisions, it might look more appealing to avoid intersections and visualize non-colliding hand motions. However, correcting the position of the hand or fingers results in a visual-proprioceptive discrepancy and must be used with caution. Furthermore, the lack of haptic feedback in the virtual world might result in slower actions as a user might not know exactly when a grasp has occurred. This reduced performance could be remediated with adequate visual feedback. In this study, we analyze the performance, level of ownership, and user preference of eight different visual feedback techniques for virtual grasping. Three techniques show the tracked hand (with or without grasping feedback), even if it intersects with the grasped object. Another three techniques display a hand without intersections with the object, called outer hand, simulating the look of a real world interaction. One visualization is a compromise between the two groups, showing both a primary outer hand and a secondary tracked hand. Finally, in the last visualization the hand disappears during the grasping activity. In an experiment, users perform a pick-and-place task for each feedback technique. We use high fidelity marker-based hand tracking to control the virtual hands in real time. We found that the tracked hand visualizations result in better performance, however, the outer hand visualizations were preferred. We also find indications that ownership is higher with the outer hand visualizations. 

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