Factory: Fast Contact for Robotic Assembly
暂无分享,去创建一个
Yashraj S. Narang | D. Fox | Ankur Handa | Philipp Reist | M. Macklin | Iretiayo Akinola | Yunrong Guo | Kier Storey | Lukasz Wawrzyniak | Ádám Moravánszky | Gavriel State | Michelle Lu
[1] Yashraj S. Narang,et al. DefGraspSim: Physics-Based Simulation of Grasp Outcomes for 3D Deformable Objects , 2022, IEEE Robotics and Automation Letters.
[2] Masayoshi Tomizuka,et al. Learning Insertion Primitives with Discrete-Continuous Hybrid Action Space for Robotic Assembly Tasks , 2021, 2022 International Conference on Robotics and Automation (ICRA).
[3] Viktor Makoviychuk,et al. OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation , 2021, 2022 International Conference on Robotics and Automation (ICRA).
[4] Zilin Si,et al. Taxim: An Example-based Simulation Model for GelSight Tactile Sensors , 2021, IEEE Robotics and Automation Letters.
[5] 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.
[6] S. Schaal,et al. Residual Learning from Demonstration: Adapting DMPs for Contact-rich Manipulation , 2020, IEEE Robotics and Automation Letters.
[7] Lin Shao,et al. RoboAssembly: Learning Generalizable Furniture Assembly Policy in a Novel Multi-robot Contact-rich Simulation Environment , 2021, ArXiv.
[8] Maya Cakmak,et al. Assistive Tele-op: Leveraging Transformers to Collect Robotic Task Demonstrations , 2021, ArXiv.
[9] Joseph J. Lim,et al. Adversarial Skill Chaining for Long-Horizon Robot Manipulation via Terminal State Regularization , 2021, CoRL.
[10] Pulkit Agrawal,et al. A System for General In-Hand Object Re-Orientation , 2021, CoRL.
[11] Jing Xu,et al. Data-Efficient Hierarchical Reinforcement Learning for Robotic Assembly Control Applications , 2021, IEEE Transactions on Industrial Electronics.
[12] Philipp Reist,et al. Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning , 2021, CoRL.
[13] Miles Macklin,et al. Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning , 2021, NeurIPS Datasets and Benchmarks.
[14] Manuel Wüthrich,et al. Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger , 2021, ArXiv.
[15] Kenny Erleben,et al. Contact and friction simulation for computer graphics , 2021, SIGGRAPH Courses.
[16] Silvio Savarese,et al. iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks , 2021, CoRL.
[17] Timothy R. Langlois,et al. Intersection-free rigid body dynamics , 2021, ACM Transactions on Graphics.
[18] Jens Lambrecht,et al. Towards Real-World Force-Sensitive Robotic Assembly through Deep Reinforcement Learning in Simulations , 2021, 2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM).
[19] Yashraj S. Narang,et al. DefGraspSim: Simulation-based grasping of 3D deformable objects , 2021, ArXiv.
[20] Angel X. Chang,et al. Habitat 2.0: Training Home Assistants to Rearrange their Habitat , 2021, NeurIPS.
[21] Masayoshi Tomizuka,et al. Trajectory Optimization for Manipulation of Deformable Objects: Assembly of Belt Drive Units , 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA).
[22] Shuran Song,et al. FlingBot: The Unreasonable Effectiveness of Dynamic Manipulation for Cloth Unfolding , 2021, ArXiv.
[23] Fabio Gramazio,et al. Robotic assembly of timber joints using reinforcement learning , 2021, Automation in Construction.
[24] Roozbeh Mottaghi,et al. ManipulaTHOR: A Framework for Visual Object Manipulation , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[25] Yashraj S. Narang,et al. Sim-to-Real for Robotic Tactile Sensing via Physics-Based Simulation and Learned Latent Projections , 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA).
[26] Oleg O. Sushkov,et al. Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Study , 2021, Robotics: Science and Systems.
[27] Stefan Schaal,et al. Benchmarking Off-The-Shelf Solutions to Robotic Assembly Tasks , 2021, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[28] Michelle A. Lee,et al. Interpreting Contact Interactions to Overcome Failure in Robot Assembly Tasks , 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA).
[29] Dieter Fox,et al. ACRONYM: A Large-Scale Grasp Dataset Based on Simulation , 2020, 2021 IEEE International Conference on Robotics and Automation (ICRA).
[30] Masayoshi Tomizuka,et al. Learning Dense Rewards for Contact-Rich Manipulation Tasks , 2020, 2021 IEEE International Conference on Robotics and Automation (ICRA).
[31] Quang-Cuong Pham,et al. Learning Sequences of Manipulation Primitives for Robotic Assembly , 2020, 2021 IEEE International Conference on Robotics and Automation (ICRA).
[32] Jieliang Luo,et al. A Learning Approach to Robot-Agnostic Force-Guided High Precision Assembly , 2020, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[33] Danica Kragic,et al. Stability-Guaranteed Reinforcement Learning for Contact-Rich Manipulation , 2020, IEEE Robotics and Automation Letters.
[34] Joseph J. Lim,et al. IKEA Furniture Assembly Environment for Long-Horizon Complex Manipulation Tasks , 2019, 2021 IEEE International Conference on Robotics and Automation (ICRA).
[35] Christian Duriez,et al. On the use of simulation in robotics: Opportunities, challenges, and suggestions for moving forward , 2020, Proceedings of the National Academy of Sciences.
[36] Maria Bauza,et al. Tactile Object Pose Estimation from the First Touch with Geometric Contact Rendering , 2020, CoRL.
[37] Yashraj S. Narang,et al. STReSSD: Sim-To-Real from Sound for Stochastic Dynamics , 2020, CoRL.
[38] Oliver Kroemer,et al. Towards Robotic Assembly by Predicting Robust, Precise and Task-oriented Grasps , 2020, CoRL.
[39] Dongjun Lee,et al. Sim-to-Real Transfer of Bolting Tasks with Tight Tolerance , 2020, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[40] Lorenz Wellhausen,et al. Learning quadrupedal locomotion over challenging terrain , 2020, Science Robotics.
[41] Byron Boots,et al. Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion , 2020, CoRL.
[42] Miriam Zacksenhouse,et al. Deep Reinforcement Learning for Contact-Rich Skills Using Compliant Movement Primitives , 2020, ArXiv.
[43] K. Harada,et al. Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep Reinforcement Learning Approach , 2020, Applied Sciences.
[44] Mathieu Aubry,et al. CosyPose: Consistent multi-view multi-object 6D pose estimation , 2020, ECCV.
[45] Sergey Levine,et al. Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks , 2020, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[46] ErlebenKenny,et al. Local Optimization for Robust Signed Distance Field Collision , 2020, Proc. ACM Comput. Graph. Interact. Tech..
[47] Afsoon Afzal,et al. A Study on the Challenges of Using Robotics Simulators for Testing , 2020, ArXiv.
[48] Yu Sun,et al. Benchmarking Protocols for Evaluating Small Parts Robotic Assembly Systems , 2020, IEEE Robotics and Automation Letters.
[49] Leonidas J. Guibas,et al. SAPIEN: A SimulAted Part-Based Interactive ENvironment , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[50] Yashraj S. Narang,et al. Inferring the Material Properties of Granular Media for Robotic Tasks , 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).
[51] Nikolaus Correll,et al. Robots assembling machines: learning from the World Robot Summit 2018 Assembly Challenge , 2020, Adv. Robotics.
[52] Jeannette Bohg,et al. Learning to Scaffold the Development of Robotic Manipulation Skills , 2019, 2020 IEEE International Conference on Robotics and Automation (ICRA).
[53] Silvio Savarese,et al. Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks , 2019, IEEE Transactions on Robotics.
[54] Jakub W. Pachocki,et al. Learning dexterous in-hand manipulation , 2018, Int. J. Robotics Res..
[55] Mabel M. Zhang. Necessity for More Realistic Contact Simulation , 2020 .
[56] Timothy R. Langlois,et al. Incremental Potential Contact: Intersection- and Inversion-free, Large-Deformation Dynamics , 2020 .
[57] Hui Li,et al. Dynamic Experience Replay , 2019, CoRL.
[58] P. Allen,et al. MAT: Multi-Fingered Adaptive Tactile Grasping via Deep Reinforcement Learning , 2019, CoRL.
[59] Stefan Jeschke,et al. Small steps in physics simulation , 2019, Symposium on Computer Animation.
[60] Silvio Savarese,et al. Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks , 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[61] Myungsin Kim,et al. Data-Driven Contact Clustering for Robot Simulation , 2019, 2019 International Conference on Robotics and Automation (ICRA).
[62] Alice M. Agogino,et al. Reinforcement Learning on Variable Impedance Controller for High-Precision Robotic Assembly , 2019, 2019 International Conference on Robotics and Automation (ICRA).
[63] Jan Bender,et al. Interlinked SPH Pressure Solvers for Strong Fluid-Rigid Coupling , 2019, ACM Trans. Graph..
[64] Ken Goldberg,et al. Learning ambidextrous robot grasping policies , 2019, Science Robotics.
[65] Peter C. Horak,et al. On the Similarities and Differences Among Contact Models in Robot Simulation , 2019, IEEE Robotics and Automation Letters.
[66] Matthew T. Mason,et al. A Survey of Automated Threaded Fastening , 2019, IEEE Transactions on Automation Science and Engineering.
[67] Yevgen Chebotar,et al. Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience , 2018, 2019 International Conference on Robotics and Automation (ICRA).
[68] Oleg O. Sushkov,et al. A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning , 2018, 2019 International Conference on Robotics and Automation (ICRA).
[69] Masayoshi Tomizuka,et al. A Learning Framework for High Precision Industrial Assembly , 2019, 2019 International Conference on Robotics and Automation (ICRA).
[70] Yu Sun,et al. A dataset of daily interactive manipulation , 2018, Int. J. Robotics Res..
[71] Silvio Savarese,et al. ROBOTURK: A Crowdsourcing Platform for Robotic Skill Learning through Imitation , 2018, CoRL.
[72] N. Hogan,et al. Impedance and Interaction Control , 2018 .
[73] Alice M. Agogino,et al. Deep Reinforcement Learning for Robotic Assembly of Mixed Deformable and Rigid Objects , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[74] Xian Zhou,et al. Can robots assemble an IKEA chair? , 2018, Science Robotics.
[75] Pieter Abbeel,et al. Learning Robotic Assembly from CAD , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).
[76] Giovanni De Magistris,et al. Deep reinforcement learning for high precision assembly tasks , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[77] Martin A. Riedmiller,et al. Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards , 2017, ArXiv.
[78] Glen Berseth,et al. DeepLoco , 2017, ACM Trans. Graph..
[79] Wojciech Zaremba,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).
[80] Michiel van de Panne,et al. Learning locomotion skills using DeepRL: does the choice of action space matter? , 2016, Symposium on Computer Animation.
[81] Kevin M. Lynch,et al. Modern Robotics: Mechanics, Planning, and Control , 2017 .
[82] Alessio Rocchi,et al. Stable simulation of underactuated compliant hands , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).
[83] Eric Lengyel. Volumetric Hierarchical Approximate Convex Decomposition , 2016 .
[84] Hammad Mazhar,et al. Chrono: An Open Source Multi-physics Dynamics Engine , 2015, HPCSE.
[85] Hongyi Xu,et al. Implicit Multibody Penalty-BasedDistributed Contact , 2014, IEEE Transactions on Visualization and Computer Graphics.
[86] Blaine Lilly,et al. Mechanical Assemblies: their Design, Manufacture, and Role in Product Development , 2013 .
[87] Kris Hauser,et al. Robust Contact Generation for Robot Simulation with Unstructured Meshes , 2013, ISRR.
[88] Maud Marchal,et al. Efficient collision detection for brittle fracture , 2012, SCA '12.
[89] Jeong Kim,et al. Finite element analysis and modeling of structure with bolted joints , 2007 .
[90] C. Lacoursière. Ghosts and machines : regularized variational methods for interactive simulations of multibodies with dry frictional contacts , 2007 .
[91] Ming C. Lin,et al. A modular haptic rendering algorithm for stable and transparent 6-DOF manipulation , 2006, IEEE Transactions on Robotics.
[92] S. Buss. Introduction to Inverse Kinematics with Jacobian Transpose , Pseudoinverse and Damped Least Squares methods , 2004 .
[93] Matthew T. Mason,et al. Mechanics of Robotic Manipulation , 2001 .
[94] Louis A. Martin-Vega,et al. Industrial perspective on research needs and opportunities in manufacturing assembly , 1995 .
[95] Oussama Khatib,et al. A unified approach for motion and force control of robot manipulators: The operational space formulation , 1987, IEEE J. Robotics Autom..