Digital twin-enabled grasp outcomes assessment for unknown objects using visual-tactile fusion perception

[1]  C. Brecher,et al.  Hybrid learning-based digital twin for manufacturing process: Modeling framework and implementation , 2023, Robotics Comput. Integr. Manuf..

[2]  N. Arana-Arexolaleiba,et al.  A review on reinforcement learning for contact-rich robotic manipulation tasks , 2023, Robotics Comput. Integr. Manuf..

[3]  Ding Liu,et al.  A digital twin-based sim-to-real transfer for deep reinforcement learning-enabled industrial robot grasping , 2022, Robotics Comput. Integr. Manuf..

[4]  W. Wan,et al.  Hardware Technology of Vision-Based Tactile Sensor: A Review , 2022, IEEE Sensors Journal.

[5]  Zude Zhou,et al.  Robot learning towards smart robotic manufacturing: A review , 2022, Robotics Comput. Integr. Manuf..

[6]  Wenzhen Yuan,et al.  PoseIt: A Visual-Tactile Dataset of Holding Poses for Grasp Stability Analysis , 2022, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[7]  Wenzhen Yuan,et al.  Grasp Stability Prediction with Sim-to-Real Transfer from Tactile Sensing , 2022, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[8]  N. Lepora,et al.  Tactile Gym 2.0: Sim-to-Real Deep Reinforcement Learning for Comparing Low-Cost High-Resolution Robot Touch , 2022, IEEE Robotics and Automation Letters.

[9]  Peiyu Zeng,et al.  Bidirectional Sim-to-Real Transfer for GelSight Tactile Sensors With CycleGAN , 2022, IEEE Robotics and Automation Letters.

[10]  Lianhui Li,et al.  Digital twin in smart manufacturing , 2022, J. Ind. Inf. Integr..

[11]  Zilin Si,et al.  Taxim: An Example-based Simulation Model for GelSight Tactile Sensors , 2021, IEEE Robotics and Automation Letters.

[12]  Raj Kolamuri,et al.  Improving Grasp Stability with Rotation Measurement from Tactile Sensing , 2021, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[13]  Siyuan Dong,et al.  GelSlim 3.0: High-Resolution Measurement of Shape, Force and Slip in a Compact Tactile-Sensing Finger , 2021, 2022 International Conference on Robotics and Automation (ICRA).

[14]  Cewu Lu,et al.  RGB Matters: Learning 7-DoF Grasp Poses on Monocular RGBD Images , 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA).

[15]  Paolo Paoletti,et al.  Generation of GelSight Tactile Images for Sim2Real Learning , 2021, IEEE Robotics and Automation Letters.

[16]  Wenzhen Yuan,et al.  Simulation of Vision-based Tactile Sensors using Physics based Rendering , 2020, 2021 IEEE International Conference on Robotics and Automation (ICRA).

[17]  R. Calandra,et al.  TACTO: A Fast, Flexible, and Open-Source Simulator for High-Resolution Vision-Based Tactile Sensors , 2020, IEEE Robotics and Automation Letters.

[18]  Shuo Wang,et al.  Self-Attention Based Visual-Tactile Fusion Learning for Predicting Grasp Outcomes , 2020, IEEE Robotics and Automation Letters.

[19]  Shuo Wang,et al.  Grasp State Assessment of Deformable Objects Using Visual-Tactile Fusion Perception , 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[20]  Sergey Levine,et al.  OmniTact: A Multi-Directional High-Resolution Touch Sensor , 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[21]  Mike Lambeta,et al.  DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor With Application to In-Hand Manipulation , 2020, IEEE Robotics and Automation Letters.

[22]  Fei Tao,et al.  Make more digital twins , 2019, Nature.

[23]  Jan Peters,et al.  Grip Stabilization of Novel Objects Using Slip Prediction , 2018, IEEE Transactions on Haptics.

[24]  Fuchun Sun,et al.  PointNetGPD: Detecting Grasp Configurations from Point Sets , 2018, 2019 International Conference on Robotics and Automation (ICRA).

[25]  Ken Goldberg,et al.  Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Data , 2018, 2019 International Conference on Robotics and Automation (ICRA).

[26]  Jitendra Malik,et al.  More Than a Feeling: Learning to Grasp and Regrasp Using Vision and Touch , 2018, IEEE Robotics and Automation Letters.

[27]  Peter Corke,et al.  Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach , 2018, Robotics: Science and Systems.

[28]  Edward H. Adelson,et al.  GelSight: High-Resolution Robot Tactile Sensors for Estimating Geometry and Force , 2017, Sensors.

[29]  Andrew Owens,et al.  The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes? , 2017, CoRL.

[30]  Vincent Duchaine,et al.  Grasp stability assessment through the fusion of proprioception and tactile signals using convolutional neural networks , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[31]  Xinyu Liu,et al.  Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics , 2017, Robotics: Science and Systems.

[32]  P. Abbeel,et al.  Domain randomization for transferring deep neural networks from simulation to the real world , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[33]  P. Abbeel,et al.  Yale-CMU-Berkeley dataset for robotic manipulation research , 2017, Int. J. Robotics Res..

[34]  Leonidas J. Guibas,et al.  PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[35]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[36]  Raymond Y. K. Lau,et al.  Least Squares Generative Adversarial Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[37]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[38]  Pieter Abbeel,et al.  BigBIRD: A large-scale 3D database of object instances , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[39]  Rüdiger Dillmann,et al.  The KIT object models database: An object model database for object recognition, localization and manipulation in service robotics , 2012, Int. J. Robotics Res..

[40]  Sachin Chitta,et al.  Human-Inspired Robotic Grasp Control With Tactile Sensing , 2011, IEEE Transactions on Robotics.

[41]  Jimmy A. Jørgensen,et al.  Assessing Grasp Stability Based on Learning and Haptic Data , 2011, IEEE Transactions on Robotics.

[42]  Andrew Howard,et al.  Design and use paradigms for Gazebo, an open-source multi-robot simulator , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[43]  Joel W. Burdick,et al.  Finding antipodal point grasps on irregularly shaped objects , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.

[44]  Weifei Hu,et al.  A grasps-generation-and-selection convolutional neural network for a digital twin of intelligent robotic grasping , 2022, Robotics Comput. Integr. Manuf..

[45]  Chengrui Zhang,et al.  A robotic grasp detection method based on auto-annotated dataset in disordered manufacturing scenarios , 2022, Robotics Comput. Integr. Manuf..

[46]  José Boaventura-Cunha,et al.  Robotic grasping: from wrench space heuristics to deep learning policies , 2021, Robotics Comput. Integr. Manuf..

[47]  Robert Platt,et al.  Using Geometry to Detect Grasp Poses in 3D Point Clouds , 2015, ISRR.

[48]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.