Log in

Relevant bibliographies by topics / Sim2real / Journal articles

To see the other types of publications on this topic, follow the link: Sim2real.

Journal articles on the topic 'Sim2real'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 26 journal articles for your research on the topic 'Sim2real.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Zhong, Chengliang, Chao Yang, Fuchun Sun, Jinshan Qi, Xiaodong Mu, Huaping Liu, and Wenbing Huang. "Sim2Real Object-Centric Keypoint Detection and Description." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 5 (June 28, 2022): 5440–49. http://dx.doi.org/10.1609/aaai.v36i5.20482.

Full text

Abstract:

Keypoint detection and description play a central role in computer vision. Most existing methods are in the form of scene-level prediction, without returning the object classes of different keypoints. In this paper, we propose the object-centric formulation, which, beyond the conventional setting, requires further identifying which object each interest point belongs to. With such fine-grained information, our framework enables more downstream potentials, such as object-level matching and pose estimation in a clustered environment. To get around the difficulty of label collection in the real world, we develop a sim2real contrastive learning mechanism that can generalize the model trained in simulation to real-world applications. The novelties of our training method are three-fold: (i) we integrate the uncertainty into the learning framework to improve feature description of hard cases, e.g., less-textured or symmetric patches; (ii) we decouple the object descriptor into two independent branches, intra-object salience and inter-object distinctness, resulting in a better pixel-wise description; (iii) we enforce cross-view semantic consistency for enhanced robustness in representation learning. Comprehensive experiments on image matching and 6D pose estimation verify the encouraging generalization ability of our method. Particularly for 6D pose estimation, our method significantly outperforms typical unsupervised/sim2real methods, achieving a closer gap with the fully supervised counterpart.

APA, Harvard, Vancouver, ISO, and other styles

2

Hofer, Sebastian, Kostas Bekris, Ankur Handa, Juan Camilo Gamboa, Melissa Mozifian, Florian Golemo, Chris Atkeson, et al. "Sim2Real in Robotics and Automation: Applications and Challenges." IEEE Transactions on Automation Science and Engineering 18, no. 2 (April 2021): 398–400. http://dx.doi.org/10.1109/tase.2021.3064065.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Gomes, Daniel Fernandes, Paolo Paoletti, and Shan Luo. "Generation of GelSight Tactile Images for Sim2Real Learning." IEEE Robotics and Automation Letters 6, no. 2 (April 2021): 4177–84. http://dx.doi.org/10.1109/lra.2021.3063925.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Allamaa, Jean Pierre, Panagiotis Patrinos, Herman Van der Auweraer, and Tong Duy Son. "Sim2real for Autonomous Vehicle Control using Executable Digital Twin." IFAC-PapersOnLine 55, no. 24 (2022): 385–91. http://dx.doi.org/10.1016/j.ifacol.2022.10.314.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Liu, Wenzheng, Chun Zhao, Yue Liu, Hongwei Wang, Wei Zhao, and Heming Zhang. "Sim2real kinematics modeling of industrial robots based on FPGA-acceleration." Robotics and Computer-Integrated Manufacturing 77 (October 2022): 102350. http://dx.doi.org/10.1016/j.rcim.2022.102350.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Weibel, Jean-Baptiste, Timothy Patten, and Markus Vincze. "Addressing the Sim2Real Gap in Robotic 3-D Object Classification." IEEE Robotics and Automation Letters 5, no. 2 (April 2020): 407–13. http://dx.doi.org/10.1109/lra.2019.2959497.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Kadian, Abhishek, Joanne Truong, Aaron Gokaslan, Alexander Clegg, Erik Wijmans, Stefan Lee, Manolis Savva, Sonia Chernova, and Dhruv Batra. "Sim2Real Predictivity: Does Evaluation in Simulation Predict Real-World Performance?" IEEE Robotics and Automation Letters 5, no. 4 (October 2020): 6670–77. http://dx.doi.org/10.1109/lra.2020.3013848.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Truong, Joanne, Sonia Chernova, and Dhruv Batra. "Bi-Directional Domain Adaptation for Sim2Real Transfer of Embodied Navigation Agents." IEEE Robotics and Automation Letters 6, no. 2 (April 2021): 2634–41. http://dx.doi.org/10.1109/lra.2021.3062303.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Zhang, Tan, Kefang Zhang, Jiatao Lin, Wing-Yue Geoffrey Louie, and Hui Huang. "Sim2real Learning of Obstacle Avoidance for Robotic Manipulators in Uncertain Environments." IEEE Robotics and Automation Letters 7, no. 1 (January 2022): 65–72. http://dx.doi.org/10.1109/lra.2021.3116700.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Abascal, Juan F. P. J., Nicolas Ducros, Valeriya Pronina, Simon Rit, Pierre-Antoine Rodesch, Thomas Broussaud, Suzanne Bussod, et al. "Material Decomposition in Spectral CT Using Deep Learning: A Sim2Real Transfer Approach." IEEE Access 9 (2021): 25632–47. http://dx.doi.org/10.1109/access.2021.3056150.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Maack, Robert F., Constantin Waubert de Puiseau, Anna Sokolova, Haile Atsbha, Hasan Tercan, and Tobias Meisen. "Reducing the Sim2Real-Gap in Extrusion Blow Molding using Random Forest Regressors." Manufacturing Letters 33 (September 2022): 843–49. http://dx.doi.org/10.1016/j.mfglet.2022.07.104.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Zhang, Qi, Tao Du, and Changzheng Tian. "A Sim2real method based on DDQN for training a self-driving scale car." Mathematical Foundations of Computing 2, no. 4 (2019): 315–31. http://dx.doi.org/10.3934/mfc.2019020.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Beltran-Hernandez, Cristian C., Damien Petit, Ixchel G. Ramirez-Alpizar, and Kensuke Harada. "Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep-Reinforcement-Learning Approach." Applied Sciences 10, no. 19 (October 2, 2020): 6923. http://dx.doi.org/10.3390/app10196923.

Full text

Abstract:

Industrial robot manipulators are playing a significant role in modern manufacturing industries. Though peg-in-hole assembly is a common industrial task that has been extensively researched, safely solving complex, high-precision assembly in an unstructured environment remains an open problem. Reinforcement-learning (RL) methods have proven to be successful in autonomously solving manipulation tasks. However, RL is still not widely adopted in real robotic systems because working with real hardware entails additional challenges, especially when using position-controlled manipulators. The main contribution of this work is a learning-based method to solve peg-in-hole tasks with hole-position uncertainty. We propose the use of an off-policy, model-free reinforcement-learning method, and we bootstraped the training speed by using several transfer-learning techniques (sim2real) and domain randomization. Our proposed learning framework for position-controlled robots was extensively evaluated in contact-rich insertion tasks in a variety of environments.

APA, Harvard, Vancouver, ISO, and other styles

14

Jenkins, Porter, Hua Wei, J. Stockton Jenkins, and Zhenhui Li. "Bayesian Model-Based Offline Reinforcement Learning for Product Allocation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 12531–37. http://dx.doi.org/10.1609/aaai.v36i11.21523.

Full text

Abstract:

Product allocation in retail is the process of placing products throughout a store to connect consumers with relevant products. Discovering a good allocation strategy is challenging due to the scarcity of data and the high cost of experimentation in the physical world. Some work explores Reinforcement learning (RL) as a solution, but these approaches are often limited because of the sim2real problem. Learning policies from logged trajectories of a system is a key step forward for RL in physical systems. Recent work has shown that model-based offline RL can improve the effectiveness of offline policy estimation through uncertainty-penalized exploration. However, existing work assumes a continuous state space and access to a covariance matrix of the environment dynamics, which is not possible in the discrete case. To solve this problem, we propose a Bayesian model-based technique that naturally produces probabilistic estimates of the environment dynamics via the posterior predictive distribution, which we use for uncertainty-penalized exploration. We call our approach Posterior Penalized Offline Policy Optimization (PPOPO). We show that our world model better fits historical data due to informative priors, and that PPOPO outperforms other offline techniques in simulation and against real-world data.

APA, Harvard, Vancouver, ISO, and other styles

15

Miyanishi, Taiki, Takuya Maekawa, and Motoaki Kawanabe. "Sim2RealQA: Using Life Simulation to Solve Question Answering Real-World Events." IEEE Access 9 (2021): 75003–20. http://dx.doi.org/10.1109/access.2021.3080275.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Xiao, Wenwen, Xiangfeng Luo, and Shaorong Xie. "Feature semantic space-based sim2real decision model." Applied Intelligence, June 16, 2022. http://dx.doi.org/10.1007/s10489-022-03566-5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Mengacci, Riccardo, Grazia Zambella, Giorgio Grioli, Danilo Caporale, Manuel G. Catalano, and Antonio Bicchi. "An Open-Source ROS-Gazebo Toolbox for Simulating Robots With Compliant Actuators." Frontiers in Robotics and AI 8 (August 11, 2021). http://dx.doi.org/10.3389/frobt.2021.713083.

Full text

Abstract:

To enable the design of planning and control strategies in simulated environments before their direct application to the real robot, exploiting the Sim2Real practice, powerful and realistic dynamic simulation tools have been proposed, e.g., the ROS-Gazebo framework. However, the majority of such simulators do not account for some of the properties of recently developed advanced systems, e.g., dynamic elastic behaviors shown by all those robots that purposely incorporate compliant elements into their actuators, the so-called Articulated Soft Robots ASRs. This paper presents an open-source ROS-Gazebo toolbox for simulating ASRs equipped with the aforementioned types of compliant actuators. To achieve this result, the toolbox consists of two ROS-Gazebo modules: a plugin that implements the custom compliant characteristics of a given actuator and simulates the internal motor dynamics, and a Robotic Operation System (ROS) manager node used to organize and simplify the overall toolbox usage. The toolbox can implement different compliant joint structures to perform realistic and representative simulations of ASRs, also when they interact with the environment. The simulated ASRs can be also used to retrieve information about the physical behavior of the real system from its simulation, and to develop control policies that can be transferred back to the real world, leveraging the Sim2Real practice. To assess the versatility of the proposed plugin, we report simulations of different compliant actuators. Then, to show the reliability of the simulated results, we present experiments executed on two ASRs and compare the performance of the real hardware with the simulations. Finally, to validate the toolbox effectiveness for Sim2Real control design, we learn a control policy in simulation, then feed it to the real system in feed-forward comparing the results.

APA, Harvard, Vancouver, ISO, and other styles

18

Tsinganos, Konstantinos, Konstantinos Chatzilygeroudis, Denis Hadjivelichkov, Theodoros Komninos, Evangelos Dermatas, and Dimitrios Kanoulas. "Behavior policy learning: Learning multi-stage tasks via solution sketches and model-based controllers." Frontiers in Robotics and AI 9 (October 12, 2022). http://dx.doi.org/10.3389/frobt.2022.974537.

Full text

Abstract:

Multi-stage tasks are a challenge for reinforcement learning methods, and require either specific task knowledge (e.g., task segmentation) or big amount of interaction times to be learned. In this paper, we propose Behavior Policy Learning (BPL) that effectively combines 1) only few solution sketches, that is demonstrations without the actions, but only the states, 2) model-based controllers, and 3) simulations to effectively solve multi-stage tasks without strong knowledge about the underlying task. Our main intuition is that solution sketches alone can provide strong data for learning a high-level trajectory by imitation, and model-based controllers can be used to follow this trajectory (we call it behavior) effectively. Finally, we utilize robotic simulations to further improve the policy and make it robust in a Sim2Real style. We evaluate our method in simulation with a robotic manipulator that has to perform two tasks with variations: 1) grasp a box and place it in a basket, and 2) re-place a book on a different level within a bookcase. We also validate the Sim2Real capabilities of our method by performing real-world experiments and realistic simulated experiments where the objects are tracked through an RGB-D camera for the first task.

APA, Harvard, Vancouver, ISO, and other styles

19

Horvath, Daniel, Gabor Erdos, Zoltan Istenes, Tomas Horvath, and Sandor Foldi. "Object Detection Using Sim2Real Domain Randomization for Robotic Applications." IEEE Transactions on Robotics, 2022, 1–19. http://dx.doi.org/10.1109/tro.2022.3207619.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Shi, Haoran, Guanjun Liu, Kaiwen Zhang, Ziyuan Zhou, and Jiacun Wang. "MARL Sim2real Transfer: Merging Physical Reality With Digital Virtuality in Metaverse." IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, 1–11. http://dx.doi.org/10.1109/tsmc.2022.3229213.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Zhao, Yongqiang, Xingshuo Jing, Kun Qian, Daniel Fernandes Gomes, and Shan Luo. "Skill generalization of tubular object manipulation with tactile sensing and Sim2Real learning." Robotics and Autonomous Systems, December 2022, 104321. http://dx.doi.org/10.1016/j.robot.2022.104321.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

"Research on Force Control Based on Sim2Real Transfer for Stiffness of Thin-walled Parts." Journal of Mechanical Engineering 57, no. 17 (2021): 53. http://dx.doi.org/10.3901/jme.2021.17.053.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Liang, Ning, Sashuang Sun, Lei Zhou, Nan Zhao, Mohamed Farag Taha, Yong He, and zhengjun qiu. "High-Throughput Instance Segmentation and Shape Restoration of Overlapping Vegetable Seeds Based on Sim2real Method." SSRN Electronic Journal, 2022. http://dx.doi.org/10.2139/ssrn.4195243.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Liang, Ning, Sashuang Sun, Lei Zhou, Nan Zhao, Mohamed Farag Taha, Yong He, and Zhengjun Qiu. "High-throughput instance segmentation and shape restoration of overlapping vegetable seeds based on sim2real method." Measurement, December 2022, 112414. http://dx.doi.org/10.1016/j.measurement.2022.112414.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Xiao, Chenxi, Peng Lu, and Qizhi He. "Flying Through a Narrow Gap Using End-to-End Deep Reinforcement Learning Augmented With Curriculum Learning and Sim2Real." IEEE Transactions on Neural Networks and Learning Systems, 2021, 1–8. http://dx.doi.org/10.1109/tnnls.2021.3107742.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Albeaik, Saleh, Trevor Wu, Ganeshnikhil Vurimi, Fang‐Chieh Chou, Xiao‐Yun Lu, and Alexandre M. Bayen. "Longitudinal deep truck: Deep longitudinal model with application to sim2real deep reinforcement learning for heavy‐duty truck control in the field." Journal of Field Robotics, November 12, 2022. http://dx.doi.org/10.1002/rob.22131.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!